1. What were the four stages of development of ChatGPT?
The development of ChatGPT can be broadly categorized into four stages:
1. Pre-training:
In this initial phase, the model learns from a large corpus of text data from the internet. This unsupervised learning phase allows the model to understand language patterns, grammar, facts, and some level of reasoning ability. The model doesn't learn specific facts but rather absorbs general knowledge.
2. Fine-tuning:
After pre-training, the model undergoes fine-tuning on a narrower dataset with human reviewers following specific guidelines. This supervised learning phase helps to align the model's responses more closely with human expectations and makes it safer and more useful.
3. Reinforcement Learning from Human Feedback (RLHF):
To further improve the model, it goes through a reinforcement learning phase where human feedback is used to fine-tune its responses. Humans rate the model's outputs, and these ratings are used to adjust the model's behavior to be more aligned with user preferences.
4. Iterative Improvements:
This stage involves ongoing improvements based on user interactions, feedback, and new research. OpenAI continuously updates the model to address its limitations, enhance its capabilities, and make it more aligned with ethical standards and user expectations.
These stages collectively contribute to the development and enhancement of ChatGPT, making it a more powerful and user-friendly AI tool.
2. What is Token Classification? Explain with examples.
Token classification is a Natural Language Processing (NLP) task where individual tokens (words or subwords) in a text are classified into predefined categories. This task is fundamental in various NLP applications, including named entity recognition (NER), part-of-speech (POS) tagging, and chunking. Here's an explanation with examples:
1. Named Entity Recognition (NER):
In NER, the goal is to identify and classify proper nouns in a text into predefined categories such as names of people, organizations, locations, dates, and more.
Example:
Input: "Apple Inc. was founded by Steve Jobs."
Output:
"Apple Inc." -> Organization
"Steve Jobs" -> Person
2. Part-of-Speech (POS) Tagging:
POS tagging involves labeling each token in a sentence with its corresponding part of speech, such as noun, verb, adjective, etc.
Example:
Input: "The quick brown fox jumps over the lazy dog."
Output:
"The" -> Determiner (DT)
"quick" -> Adjective (JJ)
"brown" -> Adjective (JJ)
"fox" -> Noun (NN)
"jumps" -> Verb (VBZ)
"over" -> Preposition (IN)
"the" -> Determiner (DT)
"lazy" -> Adjective (JJ)
"dog" -> Noun (NN)
3. Chunking:
Chunking involves grouping adjacent tokens into chunks based on their POS tags. It is also known as shallow parsing.
Example:
Input: "I saw the big cat."
Output:
"I" -> [I] (NP - Noun Phrase)
"saw" -> [saw] (VP - Verb Phrase)
"the big cat" -> [the big cat] (NP - Noun Phrase)
How Token Classification Works:
Tokenization:
The text is split into tokens. This can be done at the word level, subword level, or character level.
Feature Extraction:
Features are extracted from the tokens. This can include embeddings, contextual information from surrounding words, etc.
Classification:
Each token is classified using a model (e.g., a neural network) that has been trained on labeled data. The model assigns a category to each token based on its features.
Example of a Neural Network for Token Classification:
Suppose we use a BERT model for NER. The input sentence is tokenized and fed into BERT, which produces contextualized embeddings for each token. These embeddings are then passed through a classification layer that assigns a label to each token.
Example:
Input: "Barack Obama was born in Hawaii."
Tokenized Input: ["Barack", "Obama", "was", "born", "in", "Hawaii", "."]
BERT Embeddings: [embedding_1, embedding_2, ..., embedding_7]
Classification Layer Output:
"Barack" -> Person
"Obama" -> Person
"Hawaii" -> Location
Token classification is essential for many advanced NLP tasks and is a key component in building systems that can understand and process human language.
3. What is masked language modeling?
Masked Language Modeling (MLM) is a training strategy used in natural language processing (NLP) to improve the ability of language models to understand context and predict missing words in a sentence. It is a key technique used in models like BERT (Bidirectional Encoder Representations from Transformers).
How MLM Works
Masking Tokens:
During training, some of the tokens in the input text are randomly replaced with a special [MASK] token. This means the model does not see these tokens and has to predict them based on the surrounding context.
Contextual Understanding:
The model processes the entire input sequence, including the masked tokens, and generates representations for each token based on both the left and right context (bidirectional context).
Prediction:
The model is trained to predict the original value of the masked tokens. The loss is calculated based on the difference between the predicted tokens and the actual tokens, and the model parameters are adjusted accordingly.
Example
Consider the sentence: "The quick brown fox jumps over the lazy dog."
Step-by-step process:
Original Sentence:
"The quick brown fox jumps over the lazy dog."
Masking Tokens:
"The quick brown [MASK] jumps over the [MASK] dog."
Model Input:
"The quick brown [MASK] jumps over the [MASK] dog."
Model Prediction:
The model needs to predict the masked words based on the surrounding context.
Predictions: "fox" and "lazy".
Training:
The model compares its predictions to the actual tokens ("fox" and "lazy") and adjusts its parameters to minimize the prediction error.
Importance of MLM
Bidirectional Context:
Unlike traditional language models that predict the next word in a sequence (unidirectional context), MLM allows the model to learn from both preceding and following words, providing a deeper understanding of context.
Improved Performance:
MLM has been shown to significantly enhance the performance of language models on various NLP tasks, including text classification, named entity recognition, and question answering.
Pre-training for Transfer Learning:
Models pre-trained using MLM can be fine-tuned on specific downstream tasks with smaller datasets, making them highly versatile and powerful for a wide range of applications.
Example Model: BERT
BERT is a well-known model that uses MLM for pre-training. It has demonstrated state-of-the-art performance on several NLP benchmarks due to its ability to understand the bidirectional context of words in a sentence.
Example using BERT:
Input: "The capital of France is [MASK]."
BERT predicts: "Paris"
Masked Language Modeling is thus a fundamental technique that enables modern language models to achieve high accuracy and robustness in understanding and generating human language.
4.a. What are some of the applications of Masked Language Modeling?
MLM can be used for Domain Adaptation.
MLM is useful for fine-tuning on domain specific text.
Note: If you want to train a LLM for medical documents, then you can first fine-tune the model using MLM and then fine-tuning for the specific task in hand.
BERT is an example of Masked Language Model.
BERT (Bidirectional Encoder Representations from Transformers) and Masked Language Modeling (MLM) are fundamental technologies in natural language processing (NLP) that enable a wide range of applications. Here are the top three uses of BERT and MLM:
1. Named Entity Recognition (NER)
We are mentioning NER here, but masked language models like BERT are very effective for "TOKEN CLASSIFICATION" tasks in general.
Application:
NER involves identifying and classifying entities (e.g., names of people, organizations, locations) within a text.
Example Use Case:
Healthcare: Extracting medical entities like drug names, diseases, and symptoms from clinical texts.
Finance: Identifying company names, financial terms, and locations in financial reports.
Why BERT and MLM?
BERT's bidirectional nature allows it to understand the context before and after an entity, leading to more accurate identification and classification of entities.
2. Question Answering (QA)
Application:
QA systems provide precise answers to questions based on a given context or document.
Example Use Case:
Customer Support: Automated systems that provide accurate responses to customer inquiries.
Educational Tools: Interactive learning platforms where students can ask questions and receive detailed explanations.
Why BERT and MLM?
BERT excels at understanding the context of both the question and the passage, enabling it to pinpoint the exact location of the answer within the text.
3. Text Classification
Application:
Text classification involves categorizing text into predefined labels, such as spam detection, sentiment analysis, and topic categorization.
Example Use Case:
Sentiment Analysis: Analyzing social media posts or customer reviews to determine public sentiment towards a product or service.
Spam Detection: Filtering out spam emails by classifying them based on their content.
Why BERT and MLM?
BERT's deep contextual understanding helps in accurately classifying text based on nuanced differences in language and context.
Detailed Explanation of Each Use
Named Entity Recognition (NER)
Implementation: BERT uses MLM to understand the context around the entity. For example, in the sentence "Barack Obama was born in Hawaii," BERT can use the context before and after "Barack Obama" to accurately classify it as a person.
Impact: Improved NER enhances the ability of systems to extract relevant information from unstructured data, leading to better data analysis and decision-making.
Question Answering (QA)
Implementation: BERT models are fine-tuned on QA datasets where they learn to find and extract answers from passages. For instance, given the passage "Barack Obama was born in Hawaii," and the question "Where was Barack Obama born?", BERT can accurately pinpoint "Hawaii" as the answer.
Impact: Enhanced QA systems provide users with precise information, reducing the time and effort required to find answers and improving user experience in various applications.
Text Classification
Implementation: BERT can be fine-tuned on labeled datasets for various classification tasks. For sentiment analysis, BERT can understand the sentiment expressed in a text by analyzing the context of words and phrases.
Impact: Accurate text classification enables better content filtering, sentiment analysis, and topic identification, leading to improved information management and user insights.
Conclusion
BERT and MLM have revolutionized NLP by providing robust methods for understanding and processing text. Their top applications in NER, QA, and text classification demonstrate their versatility and effectiveness in extracting and categorizing information, answering questions accurately, and understanding the sentiment and topics within text. These capabilities are crucial for advancing AI technologies and enhancing the performance of various NLP applications.
5. What is domain adaptation?
Domain adaptation is a technique in machine learning and natural language processing (NLP) where a model trained on data from one domain (source domain) is adapted to work effectively on data from a different but related domain (target domain). This is crucial when there is limited labeled data available in the target domain, but ample labeled data exists in the source domain. Domain adaptation aims to leverage the knowledge gained from the source domain to improve performance on the target domain.
Key Concepts
Source Domain: The domain with abundant labeled data used to initially train the model.
Target Domain: The domain where the model needs to be applied, typically with limited or no labeled data.
Domain Shift: Differences in data distribution between the source and target domains that can affect model performance.
Adaptation Techniques: Methods used to adjust the model to perform well on the target domain.
Types of Domain Adaptation
Supervised Domain Adaptation: There is some labeled data available in the target domain to help guide the adaptation process.
Unsupervised Domain Adaptation: No labeled data is available in the target domain, so the model relies entirely on unlabeled target data and labeled source data.
Semi-Supervised Domain Adaptation: A small amount of labeled data is available in the target domain, along with a larger amount of unlabeled data.
Techniques for Domain AdaptationFine-Tuning:
Process: Fine-tune a pre-trained model on a small amount of labeled data from the target domain.
Example: A BERT model pre-trained on general text corpora is fine-tuned on a small dataset of medical documents to adapt it to the medical domain.
Domain-Adversarial Training:
Process: Train the model to perform well on the source domain while simultaneously learning to be domain-invariant by minimizing differences between source and target domains.
Example: Using a domain classifier to distinguish between source and target data and training the feature extractor to fool this classifier.
Instance Re-weighting:
Process: Adjust the weights of source domain instances to make them more similar to target domain instances.
Example: Assign higher weights to source domain samples that are more similar to the target domain samples during training.
Feature Alignment:
Process: Align the feature representations of the source and target domains to make them more similar.
Example: Using techniques like Maximum Mean Discrepancy (MMD) to minimize the distribution difference between source and target features.
Self-Training:
Process: Use a model trained on the source domain to generate pseudo-labels for the target domain data and iteratively refine the model.
Example: Predict labels for target domain data using the source-trained model, then use these pseudo-labeled data to fine-tune the model.
Applications of Domain AdaptationHealthcare:
Adapting general NLP models to understand medical texts, clinical notes, and patient records.
Example: Using domain adaptation to apply a general language model to electronic health records (EHRs) for disease prediction.
Sentiment Analysis:
Applying a sentiment analysis model trained on movie reviews to analyze sentiments in product reviews.
Example: Adapting a model trained on social media data to perform sentiment analysis on customer feedback from different industries.
Speech Recognition:
Adapting a speech recognition model trained on clean, studio-recorded audio to work effectively on noisy, real-world audio data.
Example: Fine-tuning a model trained on standard speech datasets to recognize speech in a specific environment, such as a factory floor.
Computer Vision:
Transferring knowledge from a model trained on a dataset of street scenes to a model that needs to understand aerial imagery.
Example: Adapting an image classification model trained on natural images to classify medical images, like X-rays or MRI scans.
Conclusion
Domain adaptation is essential for applying machine learning models to new domains where labeled data is scarce. By leveraging data and models from related domains, domain adaptation techniques help improve the performance and applicability of models in real-world scenarios across various fields.
6.a. Please explain domain adaptation through MLM.
Domain adaptation through Masked Language Modeling (MLM) involves adapting a pre-trained language model to a specific domain using MLM techniques. This process leverages the ability of MLM to understand and predict masked words in a sentence, allowing the model to capture the unique linguistic characteristics and terminology of the target domain.
Steps for Domain Adaptation through MLM
Pre-training on General Data:
Initially, the language model (e.g., BERT) is pre-trained on a large and diverse corpus of general text data. This allows the model to learn general language patterns, grammar, and broad knowledge.
Domain-Specific Pre-training:
After the initial pre-training, the model is further pre-trained on a domain-specific corpus using MLM. During this phase, some words in the domain-specific texts are masked, and the model is trained to predict these masked words based on their context.
Objective: Adapt the model to understand domain-specific terminology, context, and usage patterns.
Example Workflow
Collect Domain-Specific Data:
Gather a large corpus of unlabeled text data from the target domain. For instance, if the target domain is the medical field, the corpus might include medical journals, clinical notes, and research papers.
Masking:
Randomly mask a percentage of words in the domain-specific texts. For example, in the sentence "Patients with diabetes are at higher risk of cardiovascular diseases," some words might be masked as "Patients with [MASK] are at higher [MASK] of cardiovascular diseases."
Domain-Specific MLM Training:
Train the model to predict the masked words using the domain-specific corpus. This step fine-tunes the model's embeddings to capture the domain-specific language.
Fine-Tuning for Downstream Tasks:
After domain-specific pre-training, the model can be fine-tuned on labeled data for specific NLP tasks within the domain, such as named entity recognition (NER), text classification, or question answering.
Example: Fine-tune the domain-adapted model on a labeled dataset of medical NER to identify entities like drug names, symptoms, and diagnoses.
Benefits of Domain Adaptation through MLM
Improved Understanding of Domain-Specific Language:
The model becomes more proficient in understanding and generating text that is relevant to the target domain, leading to better performance on domain-specific tasks.
Enhanced Performance on Downstream Tasks:
By adapting to the linguistic nuances of the target domain, the model achieves higher accuracy in tasks like NER, sentiment analysis, and QA within that domain.
Efficient Use of Unlabeled Data:
Domain adaptation through MLM leverages large amounts of unlabeled domain-specific data, which is often more readily available than labeled data.
Example Applications
Healthcare:
Task: Clinical Named Entity Recognition
Process: Adapt a pre-trained BERT model to the medical domain by further training it on a corpus of clinical notes using MLM. Fine-tune the adapted model on a labeled dataset of clinical entities to identify terms like diseases, medications, and procedures.
Legal:
Task: Legal Document Classification
Process: Further pre-train a general language model on a corpus of legal documents using MLM. Fine-tune the adapted model on labeled data for classifying legal documents into categories like contracts, case law, and statutes.
Finance:
Task: Financial Sentiment Analysis
Process: Adapt a general language model to the financial domain by training it on financial news articles and reports using MLM. Fine-tune the adapted model on a labeled dataset of financial sentiment to classify news articles as positive, negative, or neutral.
Conclusion
Domain adaptation through MLM is a powerful technique that leverages the contextual prediction capabilities of MLM to tailor language models to specific domains. This process enhances the model's understanding of domain-specific language and improves its performance on relevant NLP tasks, making it highly useful across various specialized fields.
6.b. Please explain domain adaptation of an LLM through Fine-Tuning.
Domain adaptation of a Large Language Model (LLM) through fine-tuning involves taking a pre-trained model and adapting it to a specific domain by further training it on a smaller, domain-specific dataset. This process enhances the model's performance on tasks related to that domain by tailoring its knowledge to the particular language and concepts prevalent in the target domain.
Steps for Domain Adaptation through Fine-Tuning
Pre-training on General Data:
Initially, the LLM (such as GPT-3 or BERT) is pre-trained on a large and diverse corpus of general text data. This extensive pre-training allows the model to learn general language patterns, grammar, and a broad spectrum of knowledge.
Collect Domain-Specific Data:
Gather a large corpus of domain-specific text. For instance, if adapting to the medical domain, this corpus might include medical literature, clinical notes, and research papers.
Fine-Tuning Process:
The pre-trained LLM is then fine-tuned on the domain-specific corpus. During this phase, the model's parameters are adjusted based on the domain-specific data to better capture the unique language and concepts of the target domain.
Detailed Workflow
Select a Pre-trained Model:
Choose a pre-trained LLM such as BERT, GPT-3, or another suitable model.
Prepare Domain-Specific Dataset:
Collect and preprocess a dataset from the target domain. Ensure the dataset is cleaned and formatted appropriately for fine-tuning.
Fine-Tuning Configuration:
Configure the fine-tuning process, including setting hyperparameters such as learning rate, batch size, and the number of training epochs. Select an appropriate training objective based on the downstream task (e.g., MLM for BERT, next-word prediction for GPT-3).
Fine-Tuning:
Train the pre-trained model on the domain-specific dataset. This involves adjusting the model’s weights based on the domain-specific data.
Example: Fine-tuning a BERT model on medical texts would involve training it to understand medical terminology and context better.
Evaluate and Optimize:
Evaluate the fine-tuned model on a validation set to ensure it performs well on domain-specific tasks. Adjust hyperparameters and retrain if necessary to optimize performance.
Deploy and Use:
Once the model is fine-tuned and evaluated, it can be deployed for domain-specific applications such as NER, sentiment analysis, text classification, or question answering.
Example Applications
Healthcare:
Task: Medical Question Answering
Process: Fine-tune a pre-trained LLM on a corpus of medical literature and clinical notes to answer medical-related questions accurately.
Legal:
Task: Legal Document Summarization
Process: Fine-tune a pre-trained LLM on a dataset of legal documents to generate concise and accurate summaries of legal texts.
Finance:
Task: Financial News Classification
Process: Fine-tune a pre-trained LLM on a dataset of financial news articles to classify news into categories like market trends, company performance, and economic indicators.
Benefits of Domain Adaptation through Fine-Tuning
Improved Task Performance:
The fine-tuned model performs significantly better on domain-specific tasks due to its tailored understanding of the domain's language and concepts.
Efficient Use of Resources:
Fine-tuning leverages the extensive pre-training of the LLM, requiring relatively less domain-specific data and computational resources compared to training a model from scratch.
Versatility:
The same pre-trained model can be adapted to various domains by fine-tuning on different domain-specific datasets, making it a versatile approach.
Challenges and Considerations
Data Availability:
Adequate domain-specific data is necessary for effective fine-tuning. The quality and quantity of this data directly impact the model's performance.
Overfitting:
There is a risk of overfitting to the domain-specific dataset, which can reduce the model's generalization capability. Regularization techniques and careful validation can help mitigate this.
Hyperparameter Tuning:
Fine-tuning requires careful selection and tuning of hyperparameters to achieve optimal performance, which can be computationally intensive and time-consuming.
Conclusion
Domain adaptation of an LLM through fine-tuning is a powerful method to tailor pre-trained models to specific domains. By further training on domain-specific data, the model becomes proficient in handling specialized language and tasks, making it highly effective for applications in healthcare, legal, finance, and other fields. This approach leverages the strengths of large-scale pre-training while adapting to the unique needs of different domains.
7. What is BLEU metric as seen for language translation?
8. What is ROUGE metric as seen for text summarization?
The ROUGE (Recall-Oriented Understudy for Gisting Evaluation) metric is a set of metrics used to evaluate the quality of text summarization and machine-generated text against reference summaries. ROUGE measures the overlap of n-grams, word sequences, and word pairs between the machine-generated summary and the reference (human-created) summary. It is widely used for assessing the performance of summarization systems.
Key Variants of ROUGE
ROUGE-N:
Measures the overlap of n-grams between the machine-generated summary and the reference summary.
ROUGE-1: Measures overlap of unigrams (individual words).
ROUGE-2: Measures overlap of bigrams (two-word sequences).
Higher-order ROUGE-N (e.g., ROUGE-3) can be used, but ROUGE-1 and ROUGE-2 are the most common.
ROUGE-L:
Measures the longest common subsequence (LCS) between the machine-generated summary and the reference summary.
Considers sentence-level structure similarity in addition to n-gram overlap.
ROUGE-W:
Weighted longest common subsequence, which emphasizes contiguous LCS matches, giving higher scores to longer contiguous matches.
ROUGE-S:
Measures the overlap of skip-bigrams, which are pairs of words in the correct order, allowing for gaps in between.
ROUGE-S4: Measures overlap with a maximum gap of 4 words.
Calculation of ROUGE Scores
ROUGE-N:
Calculate the precision, recall, and F1-score for n-gram overlaps.
Precision
=
Number of overlapping n-grams
-----------------------------------
Total number of n-grams in machine-generated summary
Recall
=
Number of overlapping n-grams
--------------------------------------
Total number of n-grams in reference summary
F1-score
= (2⋅Precision⋅Recall) / (Precision + Recall)
ROUGE-L:
Identify the longest common subsequence (LCS) and calculate precision, recall, and F1-score based on the length of the LCS.
ROUGE-W:
Calculate the weighted LCS, emphasizing longer contiguous matches.
ROUGE-S:
Calculate the overlap of skip-bigrams with a specified maximum gap between words.
Example
Consider a reference summary (RS) and a machine-generated summary (MS):
RS: "The cat is on the mat"
MS: "The cat sat on the mat"
ROUGE-1:
Unigrams in RS: {the, cat, is, on, the, mat}
Unigrams in MS: {the, cat, sat, on, the, mat}
Overlapping unigrams: {the, cat, on, the, mat}
Precision: 5/6 ≈ 0.833
Recall: 5/6 ≈ 0.833
F1-score: 0.833
ROUGE-2:
Bigrams in RS: {the cat, cat is, is on, on the, the mat}
Bigrams in MS: {the cat, cat sat, sat on, on the, the mat}
Overlapping bigrams: {the cat, on the, the mat}
Precision: 3/5 = 0.6
Recall: 3/5 = 0.6
F1-score: 0.6
Advantages of ROUGE
Easy to Compute:
ROUGE is straightforward to calculate and can be automated, making it suitable for large-scale evaluations.
Multiple Variants:
Different ROUGE variants provide flexibility to evaluate different aspects of summarization quality.
Widely Used:
ROUGE is a standard metric in the field of text summarization, making it easy to compare results across studies.
Limitations of ROUGE
Ignores Semantics:
ROUGE focuses on lexical overlap and does not account for semantic similarity or paraphrasing.
Sensitive to Length:
ROUGE can be biased by the length of the summaries, with longer summaries potentially scoring higher due to more n-grams.
Reference Dependency:
The quality of ROUGE scores depends heavily on the quality and number of reference summaries.
Conclusion
ROUGE is a crucial metric for evaluating text summarization systems, offering a reliable way to measure the overlap between machine-generated summaries and human-created reference summaries. Despite its limitations, ROUGE remains a widely accepted standard due to its simplicity and effectiveness in capturing n-gram and subsequence overlaps.
9. What is an auto-regressive LLM?
An auto-regressive Large Language Model (LLM) is a type of language model that generates text by predicting the next token in a sequence based on the tokens that have already been generated. This process continues iteratively until the entire desired sequence of text is produced. In an auto-regressive model, each token is generated one at a time, with each new token dependent on the preceding context, making the generation process inherently sequential.
Key Features of Auto-Regressive LLMs
Sequential Generation:
Text is generated one token at a time in a left-to-right manner (or sometimes right-to-left, depending on the implementation).
Each token prediction is based on all previously generated tokens, ensuring that the output is contextually coherent.
Probability Distribution:
The model outputs a probability distribution over the vocabulary for the next token, given the current sequence of tokens.
The token with the highest probability is typically chosen as the next token, although sampling strategies (e.g., temperature sampling, top-k sampling) can be used for more diverse outputs.
Training:
Auto-regressive LLMs are typically trained using a large corpus of text where the task is to predict the next token given the previous tokens.
The model learns to minimize the difference between the predicted tokens and the actual tokens in the training data.
Examples of Auto-Regressive LLMs
GPT (Generative Pre-trained Transformer):
Models like GPT-2 and GPT-3 from OpenAI are classic examples of auto-regressive LLMs.
These models use the Transformer architecture and are trained on extensive datasets to generate human-like text.
RNNs and LSTMs:
Earlier auto-regressive models included Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks.
These models also generate text sequentially, although they are less commonly used for large-scale language modeling compared to Transformers.
Applications of Auto-Regressive LLMs
Text Generation:
Generating coherent and contextually relevant text for applications such as chatbots, story generation, and content creation.
Machine Translation:
Translating text from one language to another by sequentially generating the translated text.
Text Completion:
Completing a given piece of text based on its context, useful in applications like code completion and writing assistance.
Conversational AI:
Building dialogue systems and virtual assistants that can respond to user inputs in a natural and contextually appropriate manner.
Advantages of Auto-Regressive LLMs
Contextual Coherence:
Since each token is generated based on the preceding context, auto-regressive models tend to produce coherent and contextually relevant outputs.
Flexibility:
These models can be used for a wide range of NLP tasks, from text generation to translation and summarization.
Disadvantages of Auto-Regressive LLMs
Sequential Dependency:
The generation process is inherently sequential, which can be slow, especially for long sequences.
Error Propagation:
Errors in early tokens can propagate through the sequence, potentially degrading the quality of the output.
Example of Auto-Regressive Text Generation
Consider generating text with an auto-regressive LLM like GPT-3. Given the initial prompt "Once upon a time," the model generates the next token, which might be "there," and then continues to generate subsequent tokens based on the growing context:
Prompt: "Once upon a time"
Next token: "there"
Sequence so far: "Once upon a time there"
Next token: "was"
Sequence so far: "Once upon a time there was"
And so on...
Each token is generated based on the entire sequence of previous tokens, ensuring the generated text is coherent and contextually appropriate.
Conclusion
Auto-regressive LLMs, such as GPT-3, generate text by predicting each subsequent token based on the tokens generated so far. This sequential, context-dependent generation process makes them highly effective for tasks requiring coherent and contextually relevant text, though it also introduces computational challenges due to the inherently sequential nature of the generation process.
10. What is causal language modeling?
Causal language modeling is a type of language modeling where the model predicts the next token in a sequence based only on the previous tokens, adhering to a cause-and-effect structure. This is often used in auto-regressive models where the text is generated one token at a time, with each token prediction depending solely on the tokens that have been generated before it.
Key Characteristics of Causal Language Modeling
Auto-Regressive Nature:
The model generates text sequentially, one token at a time.
Each token is predicted based on the sequence of preceding tokens.
Unidirectional Context:
The model looks only at the left context (past tokens) to predict the next token.
This unidirectional approach ensures that the model can be used for text generation tasks where future context is not available during prediction.
Training Objective:
The model is trained to maximize the likelihood of each token in the training data given all previous tokens.
The objective can be formalized as minimizing the negative log-likelihood:
Example of Causal Language Modeling
Consider a sequence of tokens "The cat sat on the mat." In causal language modeling, the model would learn to predict each token based on the preceding tokens:
Given "The," predict "cat"
Given "The cat," predict "sat"
Given "The cat sat," predict "on"
And so on.
Applications of Causal Language Modeling
Text Generation:
Used in generating coherent and contextually relevant text for applications like chatbots, content creation, and story generation.
Example: GPT-3, which can generate human-like text based on a given prompt.
Machine Translation:
Useful in translating text by sequentially generating the translated output.
Autocompletion:
Assists in code and text autocompletion, providing suggestions based on the text typed so far.
Dialogue Systems:
Powers conversational agents that generate responses based on the preceding dialogue context.
Example Models Using Causal Language Modeling
GPT (Generative Pre-trained Transformer):
GPT models (GPT-2, GPT-3) are prime examples, trained using a causal language modeling objective to generate text in an auto-regressive manner.
RNNs and LSTMs:
Earlier models like Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks also used causal language modeling principles.
Advantages of Causal Language Modeling
Natural Text Generation:
Generates text that flows naturally and is contextually coherent, as each token is based on preceding context.
Flexibility:
Can be adapted for various tasks requiring sequential text generation.
Disadvantages of Causal Language Modeling
Sequential Dependency:
Generation is inherently sequential, which can be computationally slow, especially for long sequences.
Error Propagation:
Errors in early predictions can propagate and affect the quality of subsequent tokens.
Conclusion
Causal language modeling is a fundamental approach in natural language processing that underpins many powerful text generation models, including the GPT series. By predicting each token based on preceding tokens, it ensures coherent and contextually relevant text generation, making it suitable for a wide range of applications from text completion to dialogue systems.
11. What is extractive question answering? Which type of model will work for this problem best?
Extractive question answering (QA) is a task in natural language processing (NLP) where the goal is to extract a span of text from a given document or context that directly answers a given question. Unlike generative question answering, which involves generating a new response, extractive QA focuses on finding and highlighting the exact segment of the text that contains the answer.
Key Characteristics of Extractive Question Answering
Span Extraction:
The model identifies a contiguous span of text within the document that answers the question.
The span is typically represented by start and end indices in the document.
Context and Question:
The model receives both the context (the passage or document) and the question.
The task is to locate the exact part of the context that answers the question.
Evaluation:
Performance is often measured using metrics like exact match (EM) and F1 score, which compare the predicted span to the ground truth span.
Example
Given the context:
"OpenAI was founded in December 2015 with the goal of promoting and developing friendly AI."
And the question:
"When was OpenAI founded?"
The extractive QA system should identify and extract the span:
"December 2015."
Best Models for Extractive Question Answering
Transformers-based models, particularly those that use a masked language model (MLM) objective during pre-training and can handle span-based predictions, work best for extractive QA. Some of the most effective models include:
BERT (Bidirectional Encoder Representations from Transformers):
BERT is highly effective for extractive QA due to its bidirectional attention mechanism, which allows it to understand the context and relationships between words deeply.
Fine-tuning BERT on QA datasets like SQuAD (Stanford Question Answering Dataset) has yielded state-of-the-art results.
RoBERTa (Robustly Optimized BERT Pretraining Approach):
RoBERTa is an optimized version of BERT with improvements in training methodology, making it even more powerful for extractive QA tasks.
ALBERT (A Lite BERT):
ALBERT is a lighter version of BERT with parameter-sharing techniques that reduce the model size and improve training efficiency while maintaining performance.
DistilBERT:
DistilBERT is a distilled version of BERT that is smaller and faster while retaining much of BERT’s accuracy, making it suitable for resource-constrained environments.
How These Models Work for Extractive QA
Input Representation:
The context and question are concatenated and tokenized. Special tokens like [CLS] (classification token) and [SEP] (separator token) are used to structure the input.
Token Embeddings:
Each token is converted into embeddings that include positional and segment embeddings to distinguish between the context and the question.
Transformer Layers:
The token embeddings pass through multiple layers of Transformer encoders that apply self-attention mechanisms to capture relationships between tokens.
Span Prediction:
The final hidden states corresponding to each token are used to predict the start and end positions of the answer span. Typically, two linear layers are used for this purpose:
One layer predicts the probability of each token being the start of the answer.
Another layer predicts the probability of each token being the end of the answer.
Example Workflow
Input:
Context: "OpenAI was founded in December 2015 with the goal of promoting and developing friendly AI."
Question: "When was OpenAI founded?"
Tokenization:
Tokens: ["[CLS]", "When", "was", "OpenAI", "founded", "?", "[SEP]", "OpenAI", "was", "founded", "in", "December", "2015", "with", "the", "goal", "of", "promoting", "and", "developing", "friendly", "AI", ".", "[SEP]"]
Model Processing:
The tokens pass through the Transformer layers, capturing contextual information.
Span Prediction:
The model predicts start and end positions for the answer span within the context.
Output:
Start: Token "December"
End: Token "2015"
Extracted Answer: "December 2015"
Conclusion
Extractive question answering involves identifying and extracting a span of text from a given context that answers a question. Transformer-based models like BERT, RoBERTa, ALBERT, and DistilBERT are particularly well-suited for this task due to their ability to understand and represent complex contextual relationships between words. By fine-tuning these models on QA-specific datasets, they can achieve high accuracy and provide precise answers to extractive QA problems.
12. When is it approriate or required to fine-tune an LLM? And when it isn't?
Fine-tuning a Large Language Model (LLM) is appropriate or required depending on the specific use case, the available data, and the desired performance. Here’s a breakdown of scenarios where fine-tuning an LLM is typically beneficial, as well as situations where it may not be necessary or suitable:
When to Fine-Tune an LLM:
Task-Specific Adaptation:
Specific NLP Tasks: Fine-tuning is crucial when the LLM needs to perform a task-specific function, such as sentiment analysis, named entity recognition, text classification, or question answering.
Domain-Specific Tasks: When the task involves specialized domains (e.g., medical texts, legal documents), fine-tuning helps adapt the model to the vocabulary and nuances of that domain.
Performance Enhancement:
Improving Accuracy: Fine-tuning allows the model to learn from task-specific data, potentially improving performance metrics such as accuracy, precision, recall, or F1 score on the task at hand.
Optimizing Outputs: It helps in generating more contextually relevant and accurate responses in applications like chatbots or dialogue systems.
Data Size and Diversity:
Data Availability: When there is ample task-specific data available for fine-tuning, it can help the model generalize better to the specific requirements of the task.
Data Diversity: Fine-tuning can also be beneficial when the data distribution differs significantly from the pre-training data used by the LLM, ensuring better adaptation to varied inputs.
Resource Constraints:
Computational Efficiency: Fine-tuning can make the model more computationally efficient for inference on specific tasks, especially when compared to training from scratch.
When Fine-Tuning May Not Be Necessary:
General Text Generation:
Unstructured Text: If the goal is general text generation or language modeling without specific task requirements, fine-tuning may not be necessary. Pre-trained models like GPT can generate coherent text without additional fine-tuning.
Limited Task-Specific Data:
Data Scarcity: If task-specific data is limited or if the task can be sufficiently addressed by the generic capabilities of the pre-trained LLM, fine-tuning may not provide significant benefits and could risk overfitting to the small dataset.
Time and Resource Constraints:
Limited Resources: Fine-tuning requires resources for training, validation, and parameter tuning. If resources are limited, it may be more practical to use a pre-trained model as-is for inference.
Overfitting Concerns:
Task Complexity: For simpler tasks or tasks where the model's pre-trained capabilities are already sufficient, fine-tuning could lead to overfitting or unnecessary complexity.
Considerations for Fine-Tuning:
Task-Specific Evaluation: Evaluate whether fine-tuning improves performance metrics relevant to the task, such as accuracy or F1 score.
Data Quality and Size: Assess the quality and quantity of task-specific data available for fine-tuning.
Computational Resources: Consider the computational resources needed for fine-tuning, including training time, hardware requirements, and maintenance costs.
Domain and Task Specificity: Fine-tuning is particularly effective when the task requires specialized knowledge or context that is not adequately covered by the general pre-training data of the LLM.
In summary, fine-tuning an LLM is most beneficial when adapting it to specific NLP tasks, improving task-specific performance metrics, and leveraging domain-specific or task-specific data to enhance model capabilities. However, it may not be necessary for general text generation tasks or when task-specific data is scarce or the pre-trained model already performs well enough.
13.a. What all models are available from OpenAI?
As of my last update in January 2022, OpenAI has developed and released several notable models. Here are some of the key models available from OpenAI:
GPT (Generative Pre-trained Transformer) Series:
GPT-2: Released in 2019, it was a significant advancement in natural language processing, capable of generating coherent and contextually relevant text.
GPT-3: Released in 2020, GPT-3 is a more powerful iteration with 175 billion parameters, enabling it to perform a wide range of NLP tasks, including translation, question answering, and text completion.
CLIP (Contrastive Language-Image Pre-training):
CLIP is a model released in 2021 that learns visual concepts from natural language descriptions and performs well on zero-shot and few-shot learning tasks for images.
DALL-E:
DALL-E, also released in 2021, generates images from textual descriptions using a variant of the GPT-3 architecture trained on a large dataset of text-image pairs.
Codex:
Codex, launched in 2021, is based on the GPT-3 architecture and is designed specifically for programming tasks. It can understand and generate code across various programming languages.
Jukebox:
Jukebox, released in 2020, generates music, including singing in multiple genres and styles, based on lyrics and genre prompts.
MuseNet:
MuseNet, introduced in 2019, is a deep neural network that generates musical compositions with a range of instruments and styles.
OpenAI API:
OpenAI provides an API that allows developers to access and integrate some of these models into their applications, enabling powerful AI-driven capabilities for various tasks.
These models represent OpenAI's advancements in natural language understanding, image generation, music generation, and more, leveraging large-scale deep learning techniques to achieve impressive results in various domains. For the most current and detailed information, it's best to check OpenAI's official announcements and publications.
13.b. What all models are there as in the series: ada, babbage, curie, da vinci, GPT 3.5 Turbo, GPT 4?
OpenAI provides several different model series under their API offerings, each with varying levels of capability and performance. These models are named after famous historical figures and are generally categorized by their complexity and capability. Here is a brief overview of these models:
Ada:
Ada: Named after Ada Lovelace, this is the fastest and most cost-effective model available. It is suitable for tasks requiring high throughput and lower latency, such as simple classification tasks, parsing text, and more straightforward content generation.
Babbage:
Babbage: Named after Charles Babbage, this model offers a balance between performance and cost. It is suitable for tasks that require more understanding and complexity than Ada can provide, such as moderate content generation and classification tasks with some complexity.
Curie:
Curie: Named after Marie Curie, this model provides more power and depth compared to Babbage. It is well-suited for more complex NLP tasks, such as summarization, moderate text generation, sentiment analysis, and understanding nuanced instructions.
Davinci:
Davinci: Named after Leonardo da Vinci, this is the most capable and powerful model in the series. It excels at tasks requiring a deep understanding of language, complex content generation, and highly nuanced and contextually aware interactions. It is ideal for applications like detailed content creation, complex problem solving, and intricate language comprehension.
Summary of Use Cases
Ada: Best for tasks requiring high speed and cost-efficiency. Examples include simple classification tasks, parsing, and straightforward data extraction.
Babbage: Good for tasks needing a balance of performance and cost. Suitable for moderate content generation, and more complex classification and parsing tasks.
Curie: Ideal for tasks requiring a deeper understanding and more complex NLP capabilities. Examples include summarization, complex text generation, and sentiment analysis.
Davinci: Optimal for tasks demanding the highest level of understanding and nuance. Suitable for detailed content creation, intricate language tasks, and sophisticated problem-solving.
These models are accessible via the OpenAI API, allowing developers to choose the model that best fits their specific needs in terms of performance, cost, and task complexity.
OpenAI offers additional models beyond the Ada, Babbage, Curie, and Davinci series. Here are some of the more advanced models:
GPT-3.5 Turbo:
This is an improved and more efficient version of GPT-3, offering better performance and cost-efficiency for a variety of tasks.
GPT-4:
GPT-4 is a significant advancement over previous versions, offering better understanding, generation, and contextual awareness. It can handle more complex and nuanced tasks with greater accuracy and relevance.
Summary of Advanced Models
GPT-3.5 Turbo:
An enhanced version of GPT-3 designed for improved performance and efficiency. Suitable for a wide range of tasks including more complex text generation, dialogue, and other advanced NLP applications.
GPT-4:
The latest and most advanced model, capable of understanding and generating human-like text with high accuracy and coherence. It excels in complex problem-solving, detailed content creation, and intricate language tasks.
Key Differences
Performance:
Models like GPT-3.5 Turbo and GPT-4 offer higher performance and better handling of complex queries compared to earlier models like Ada, Babbage, Curie, and Davinci.
Contextual Understanding:
These newer models have improved contextual understanding and can maintain coherence over longer interactions or more complicated prompts.
Efficiency:
Newer models are optimized for efficiency, providing better results at potentially lower computational costs.
These models are also available through OpenAI's API, allowing users to choose the model that best fits their needs based on the complexity and requirements of their tasks.
Tags: Technology,Interview Preparation,Generative AI,Large Language Models,
Step 1: Create an API key for free by logging into Google AI Studio
A:
B:
C:
D:
E:
F: Your free API key is created. Copy it and save it somewhere.
Trying a couple of things...
import google.generativeai as genai
API_KEY = 'A...o'
genai.configure(api_key=API_KEY)
model = genai.GenerativeModel()
response = model.generate_content('Teach me about how an LLM works')
print(response.text)
**Understanding Large Language Models (LLMs)**
**Introduction:**
LLMs are advanced machine learning models trained on vast amounts of text data. They can generate human-like text, translate languages, write different types of content, and perform various other text-related tasks.
**Working Principle:**
LLMs are trained on billions of words of text using a technique called transformer neural networks. These networks process sequential data and learn the relationships between words and phrases within the text.
**Training Process:**
1. **Massive Data Collection:** LLMs are trained on enormous datasets of text from various sources, such as books, articles, news, and social media posts.
2. **Tokenization:** The text is broken down into individual words and phrases called "tokens."
3. **Encoding:** Tokens are converted into numerical representations using embedding techniques.
4. **Transformer Architecture:** The transformer neural network processes the encoded tokens, identifying patterns and learning relationships within the text.
5. **Parameter Optimization:** The model adjusts its internal parameters (weights and biases) to minimize a loss function, which measures how well it predicts the next words in the text.
6. **Fine-tuning:** After general training, the LLM can be fine-tuned for specific tasks, such as language translation or content generation.
**Capabilities:**
* **Text Generation:** LLMs can create original, fluent, and grammatically correct text in response to a given prompt.
* **Language Translation:** They can translate text from one language to another with high accuracy.
* **Question Answering:** LLMs can extract information from text and answer questions related to the content.
* **Summarization:** They can condense large amounts of text into concise and informative summaries.
* **Content Creation:** LLMs can generate various types of content, including articles, stories, poems, and website copy.
**Applications:**
* **Natural Language Processing (NLP):** Sentiment analysis, text classification, chatbots
* **Content Generation:** Creative writing, marketing materials, news articles
* **Education:** Language learning, essay writing assistance
* **Research:** Literature analysis, data extraction
* **Customer Service:** Automated response systems, chat support
**Limitations:**
* **Bias:** LLMs can exhibit biases present in their training data.
* **Factual Errors:** They are not immune to factual errors in the sources they are trained on.
* **Limited Contextual Understanding:** They may struggle to understand the full context of complex text.
* **Ethics:** The use of LLMs raises ethical concerns about authorship, misinformation, and deepfakes.
Listing all models
import pprint
for model in genai.list_models():
pprint.pprint(model)
Model(name='models/chat-bison-001',
base_model_id='',
version='001',
display_name='PaLM 2 Chat (Legacy)',
description='A legacy text-only model optimized for chat conversations',
input_token_limit=4096,
output_token_limit=1024,
supported_generation_methods=['generateMessage', 'countMessageTokens'],
temperature=0.25,
top_p=0.95,
top_k=40)
Model(name='models/text-bison-001',
base_model_id='',
version='001',
display_name='PaLM 2 (Legacy)',
description='A legacy model that understands text and generates text as an output',
input_token_limit=8196,
output_token_limit=1024,
supported_generation_methods=['generateText', 'countTextTokens', 'createTunedTextModel'],
temperature=0.7,
top_p=0.95,
top_k=40)
Model(name='models/embedding-gecko-001',
base_model_id='',
version='001',
display_name='Embedding Gecko',
description='Obtain a distributed representation of a text.',
input_token_limit=1024,
output_token_limit=1,
supported_generation_methods=['embedText', 'countTextTokens'],
temperature=None,
top_p=None,
top_k=None)
Model(name='models/gemini-1.0-pro',
base_model_id='',
version='001',
display_name='Gemini 1.0 Pro',
description='The best model for scaling across a wide range of tasks',
input_token_limit=30720,
output_token_limit=2048,
supported_generation_methods=['generateContent', 'countTokens'],
temperature=0.9,
top_p=1.0,
top_k=None)
Model(name='models/gemini-1.0-pro-001',
base_model_id='',
version='001',
display_name='Gemini 1.0 Pro 001 (Tuning)',
description=('The best model for scaling across a wide range of tasks. This is a stable '
'model that supports tuning.'),
input_token_limit=30720,
output_token_limit=2048,
supported_generation_methods=['generateContent', 'countTokens', 'createTunedModel'],
temperature=0.9,
top_p=1.0,
top_k=None)
Model(name='models/gemini-1.0-pro-latest',
base_model_id='',
version='001',
display_name='Gemini 1.0 Pro Latest',
description=('The best model for scaling across a wide range of tasks. This is the latest '
'model.'),
input_token_limit=30720,
output_token_limit=2048,
supported_generation_methods=['generateContent', 'countTokens'],
temperature=0.9,
top_p=1.0,
top_k=None)
Model(name='models/gemini-1.0-pro-vision-latest',
base_model_id='',
version='001',
display_name='Gemini 1.0 Pro Vision',
description='The best image understanding model to handle a broad range of applications',
input_token_limit=12288,
output_token_limit=4096,
supported_generation_methods=['generateContent', 'countTokens'],
temperature=0.4,
top_p=1.0,
top_k=32)
Model(name='models/gemini-1.5-flash',
base_model_id='',
version='001',
display_name='Gemini 1.5 Flash',
description='Fast and versatile multimodal model for scaling across diverse tasks',
input_token_limit=1048576,
output_token_limit=8192,
supported_generation_methods=['generateContent', 'countTokens'],
temperature=1.0,
top_p=0.95,
top_k=64)
Model(name='models/gemini-1.5-flash-001',
base_model_id='',
version='001',
display_name='Gemini 1.5 Flash 001',
description='Fast and versatile multimodal model for scaling across diverse tasks',
input_token_limit=1048576,
output_token_limit=8192,
supported_generation_methods=['generateContent', 'countTokens', 'createCachedContent'],
temperature=1.0,
top_p=0.95,
top_k=64)
Model(name='models/gemini-1.5-flash-latest',
base_model_id='',
version='001',
display_name='Gemini 1.5 Flash Latest',
description='Fast and versatile multimodal model for scaling across diverse tasks',
input_token_limit=1048576,
output_token_limit=8192,
supported_generation_methods=['generateContent', 'countTokens'],
temperature=1.0,
top_p=0.95,
top_k=64)
Model(name='models/gemini-1.5-pro',
base_model_id='',
version='001',
display_name='Gemini 1.5 Pro',
description='Mid-size multimodal model that supports up to 1 million tokens',
input_token_limit=2097152,
output_token_limit=8192,
supported_generation_methods=['generateContent', 'countTokens'],
temperature=1.0,
top_p=0.95,
top_k=64)
Model(name='models/gemini-1.5-pro-001',
base_model_id='',
version='001',
display_name='Gemini 1.5 Pro 001',
description='Mid-size multimodal model that supports up to 1 million tokens',
input_token_limit=2097152,
output_token_limit=8192,
supported_generation_methods=['generateContent', 'countTokens', 'createCachedContent'],
temperature=1.0,
top_p=0.95,
top_k=64)
Model(name='models/gemini-1.5-pro-latest',
base_model_id='',
version='001',
display_name='Gemini 1.5 Pro Latest',
description='Mid-size multimodal model that supports up to 1 million tokens',
input_token_limit=2097152,
output_token_limit=8192,
supported_generation_methods=['generateContent', 'countTokens'],
temperature=1.0,
top_p=0.95,
top_k=64)
Model(name='models/gemini-pro',
base_model_id='',
version='001',
display_name='Gemini 1.0 Pro',
description='The best model for scaling across a wide range of tasks',
input_token_limit=30720,
output_token_limit=2048,
supported_generation_methods=['generateContent', 'countTokens'],
temperature=0.9,
top_p=1.0,
top_k=None)
Model(name='models/gemini-pro-vision',
base_model_id='',
version='001',
display_name='Gemini 1.0 Pro Vision',
description='The best image understanding model to handle a broad range of applications',
input_token_limit=12288,
output_token_limit=4096,
supported_generation_methods=['generateContent', 'countTokens'],
temperature=0.4,
top_p=1.0,
top_k=32)
Model(name='models/embedding-001',
base_model_id='',
version='001',
display_name='Embedding 001',
description='Obtain a distributed representation of a text.',
input_token_limit=2048,
output_token_limit=1,
supported_generation_methods=['embedContent'],
temperature=None,
top_p=None,
top_k=None)
Model(name='models/text-embedding-004',
base_model_id='',
version='004',
display_name='Text Embedding 004',
description='Obtain a distributed representation of a text.',
input_token_limit=2048,
output_token_limit=1,
supported_generation_methods=['embedContent'],
temperature=None,
top_p=None,
top_k=None)
Model(name='models/aqa',
base_model_id='',
version='001',
display_name='Model that performs Attributed Question Answering.',
description=('Model trained to return answers to questions that are grounded in provided '
'sources, along with estimating answerable probability.'),
input_token_limit=7168,
output_token_limit=1024,
supported_generation_methods=['generateAnswer'],
temperature=0.2,
top_p=1.0,
top_k=40)
Question 1: Write a CREATE query having the following nodes and the relationship from ROOT to other nodes is 'HAS_CHILD'.
ROOT
|--BROKER
|--PROVIDER
|--MEMBER
Answer:
CREATE (root:ROOT),
(broker:BROKER),
(provider:PROVIDER),
(member:MEMBER),
(root)-[:HAS_CHILD]->(broker),
(root)-[:HAS_CHILD]->(provider),
(root)-[:HAS_CHILD]->(member)
~~~
Question 2: Write a DELETE query to delete all nodes and relationships in a graph.
Answer:
MATCH (n) DETACH DELETE n
Ref
~~~
Question 3: Write a query to get a count for all nodes of a given label:
Answer:
MATCH (n:Person)
RETURN count(n) as count
Ref
~~~
Question 4: There are three EPIC nodes in my graph.
Each node has a numerical property CUSTOM_ID.
Now, I want to retrieve the node with the largest CUSTOM_ID.
Answer:
MATCH (n:EPIC)
RETURN n
ORDER BY n.CUSTOM_ID DESC
LIMIT 1
~~~
Question 5: Write query to get a node by property value in Neo4j.
Answer:
MATCH (n)
WHERE n.name = 'Mark'
RETURN n
Ref
~~~
Question 6: Delete a node with a given property.
Answer:
MATCH (n:Person {name: 'Tom Hanks'})
DELETE n
Ref
~~~
Question 7: Delete ONLY nodes having label of ENTITY:
Answer:
MATCH (n:ENTITY)
DELETE n
~~~
Question 8: Return number of EPIC nodes in the knowledge graph.
Answer:
MATCH (epic:EPIC)
RETURN count(epic) as count
~~~
Question 9: Write a query to get the EPIC node with largest numerical property of CUSTOM_ID.
Answer:
MATCH (epic:EPIC)
RETURN epic
ORDER BY epic.CUSTOM_ID DESC
LIMIT 1
~~~
Question 10:
What are some of the use cases where Between Centrality Algorithm is used?
Answer:
The Betweenness Centrality Algorithm is a powerful tool used to understand the roles of nodes in a graph and their impact on the network. Here are some use cases where it finds application:
Supply Chain Risk Analysis: In supply chain processes, Betweenness Centrality helps identify critical nodes that act as bridges between different parts of the network. For example, when transporting a product internationally, it can pinpoint bottleneck nodes during cargo ship stops in intermediate ports1.
Power Grid Contingency Analysis: The algorithm is used to analyze power grid networks, identifying critical nodes that affect the flow of electricity. Due to its computational intensity, this application often requires supercomputers2.
Community Detection and Network Routing: Betweenness Centrality assists in Girvan–Newman community detection and network routing tasks. It helps find influential nodes that connect different communities or guide information flow2.
Artificial Intelligence and Skill Characterization: Skill characterization in AI relies on identifying influential nodes. Betweenness Centrality helps determine which nodes play a crucial role in spreading information or resources2.
Epidemiology and Rumor Spreading: In epidemiology, it identifies nodes that influence the spread of diseases. Similarly, it helps analyze rumor propagation in social networks1.
Transportation Networks: The algorithm is applied to transportation networks, such as road or rail systems, to find critical nodes affecting traffic flow or resource distribution1.
Remember, Betweenness Centrality is about detecting nodes that serve as bridges, allowing information or resources to flow efficiently across a graph.
1: graphable.ai
2: computationalsocialnetworks.springeropen.com
3: nature.com
---
1.
Graph Machine Learning: Take Graph Data to the Next Level by Applying Machine Learning Techniques and Algorithms
Enrico Deusebio, 2021
2.
Graph-Powered Machine Learning
Alessandro Negro, 2021
3.
Graph Representation Learning
William L. Hamilton, 2020
4.
Deep Learning on Graphs
Jiliang Tang, 2021
5.
Graph-Powered Analytics and Machine Learning with TigerGraph
Alexander Thomas, 2023
6.
Graph Neural Networks: Foundations, Frontiers, and Applications
2022
7.
Graph Algorithms: Practical Examples in Apache Spark and Neo4j
Amy E. Hodler, 2019
8.
Building Knowledge Graphs
Jim Webber, 2023
9.
Graph Algorithms for Data Science: With Examples in Neo4j
Tomaž Bratanic, 2024
10.
Graph Neural Networks in Action
Keita Broadwater, 2024
11.
Hands-On Graph Neural Networks Using Python: Practical Techniques and Architectures for Building Powerful Graph and Deep Learning Apps with PyTorch
Maxime Labonne, 2023
12.
The Practitioner's Guide to Graph Data: Applying Graph Thinking and Graph Technologies to Solve Complex Problems
Denise Koessler Gosnell, 2020
13.
Algorithms in C, Part 5: Graph Algorithms
Robert Sedgewick, 2001
14.
Mining of Massive Datasets
Jeffrey Ullman, 2011
15.
Machine Learning for Text
Charu C. Aggarwal, 2018
16.
Knowledge Graphs: Fundamentals, Techniques, and Applications
Craig A. Knoblock, 2021
17.
Networks, Crowds, and Markets: Reasoning about a Highly Connected World
Jon Kleinberg, 2010
18.
Graph-based Natural Language Processing and Information Retrieval
Dragomir R. Radev, 2011
19.
Designing and Building Enterprise Knowledge Graphs
(Synthesis Lectures on Data, Semantics, and Knowledge)
Juan Sequeda, Ora Lassila
Morgan & Claypool (2021)
1. How comfortable are you in Python?
2. How comfortable are you in PySpark?
3. How comfortable are you in Scala?
4. And shell scripting?
---
1. What is the difference between list and tuple?
2. What are the 3 ways to work on a dataset in PySpark? (RDD, Spark SQL, and Pandas Dataframe)
3. What is lazy evaluation?
4. What is the opposite of lazy evaluation? (Eager evaluation)
5. What is the regular expression?
6. What does grep command do?
7. What does find command do?
8. What is the difference between find and grep?
9. What does sed command do?
10. What does awk command do?
11. What is narrow transformation? (Like map())
12. What is wide transformation? (Like groupby and reduceby)
13. What is the difference between narrow transformation and wide transformation?
14. How much would you give yourself in Hive?
15. Write SQL query to get current date from Hive SQL interface? (getdate(), now())
16. Take out the year from the date. (year(date_col))
17. How would you get a;b;c into:
a
b
c
Into three rows.
18. What is Spark session? (Entry point to create Spark context)
19. What is spark context?
20. Scope of which one is bigger?
21. Is there any other context object we need to know about?
22. There is a CSV file. You have to load this CSV data into an RDD, SQL dataframe, and Pandas dataframe.
THE PSYCHOLOGIST GARY Klein once told me a story about a woman who attended a family gathering. She had spent years working as a paramedic and, upon arriving at the event, took one look at her father- in-law and got very concerned.
“I don’t like the way you look,” she said.
Her father-in-law, who was feeling perfectly fine, jokingly replied,
“Well, I don’t like your looks, either.”
“No,” she insisted. “You need to go to the hospital now.”
A few hours later, the man was undergoing lifesaving surgery after an examination had revealed that he had a blockage to a major artery and was at immediate risk of a heart attack. Without his daughter-in- law’s intuition, he could have died.
What did the paramedic see? How did she predict his impending heart attack? When major arteries are obstructed, the body focuses on sending blood to critical organs and away from peripheral locations near the surface of the skin. The result is a change in the pattern of distribution of blood in the face. After many years of working with people with heart failure, the woman had unknowingly developed the ability to recognize this pattern on sight. She couldn’t explain what it was that she noticed in her father-in-law’s face, but she knew something was wrong.
~~~
...we must begin the process of behavior change with awareness
The human brain is a prediction machine. It is continuously taking in your surroundings and analyzing the information it comes across.
Whenever you experience something repeatedly—like a paramedic seeing the face of a heart attack patient or a military analyst seeing a missile on a radar screen—your brain begins noticing what is important, sorting through the details and highlighting the relevant cues, and cataloging that information for future use.
With enough practice, you can pick up on the cues that predict certain outcomes without consciously thinking about it. Automatically, your brain encodes the lessons learned through experience. We can’t always explain what it is we are learning, but learning is happening all along the way, and your ability to notice the relevant cues in a given situation is the foundation for every habit you have.
We underestimate how much our brains and bodies can do without thinking. You do not tell your hair to grow, your heart to pump, your lungs to breathe, or your stomach to digest. And yet your body handles all this and more on autopilot. You are much more than your conscious self.
Consider hunger. How do you know when you’re hungry? You don’t necessarily have to see a cookie on the counter to realize that it is time to eat. Appetite and hunger are governed nonconsciously. Your body has a variety of feedback loops that gradually alert you when it is time to eat again and that track what is going on around you and within you.
Cravings can arise thanks to hormones and chemicals circulating through your body. Suddenly, you’re hungry even though you’re not quite sure what tipped you off.
This is one of the most surprising insights about our habits: you don’t need to be aware of the cue for a habit to begin. You can notice an opportunity and take action without dedicating conscious attention to it. This is what makes habits useful.
It’s also what makes them dangerous. As habits form, your actions come under the direction of your automatic and nonconscious mind.
You fall into old patterns before you realize what’s happening. Unless someone points it out, you may not notice that you cover your mouth with your hand whenever you laugh, that you apologize before asking a question, or that you have a habit of finishing other people’s sentences.
And the more you repeat these patterns, the less likely you become to question what you’re doing and why you’re doing it.
Over time, the cues that spark our habits become so common that they are essentially invisible: the treats on the kitchen counter, the remote control next to the couch, the phone in our pocket. Our responses to these cues are so deeply encoded that it may feel like the urge to act comes from nowhere. For this reason, we must begin the process of behavior change with awareness.
THE HABITS SCORECARD
The Japanese railway system
The Japanese railway system is regarded as one of the best in the world. If you ever find yourself riding a train in Tokyo, you’ll notice that the conductors have a peculiar habit.
As each operator runs the train, they proceed through a ritual of pointing at different objects and calling out commands. When the train approaches a signal, the operator will point at it and say, “Signal is green.” As the train pulls into and out of each station, the operator will point at the speedometer and call out the exact speed. When it’s time to leave, the operator will point at the timetable and state the time. Out on the platform, other employees are performing similar actions.
Before each train departs, staff members will point along the edge of the platform and declare, “All clear!” Every detail is identified, pointed at, and named aloud.* This process, known as Pointing-and-Calling, is a safety system designed to reduce mistakes. It seems silly, but it works incredibly well. Pointing-and-Calling reduces errors by up to 85 percent and cuts accidents by 30 percent. The MTA subway system in New York City adopted a modified version that is “point-only,” and “within two years of implementation, incidents of incorrectly berthed subways fell 57 percent.”
Pointing-and-Calling is so effective because it raises the level of awareness from a nonconscious habit to a more conscious level.
Because the train operators must use their eyes, hands, mouth, and ears, they are more likely to notice problems before something goes wrong.
The more automatic a behavior becomes, the less likely we are to consciously think about it. And when we’ve done something a thousand times before, we begin to overlook things. We assume that the next time will be just like the last. We’re so used to doing what we’ve always done that we don’t stop to question whether it’s the right thing to do at all. Many of our failures in performance are largely attributable to a lack of self-awareness.
One of our greatest challenges in changing habits is maintaining awareness of what we are actually doing. This helps explain why the consequences of bad habits can sneak up on us. We need a “point-and- call” system for our personal lives. That’s the origin of the Habits Scorecard, which is a simple exercise you can use to become more aware of your behavior. To create your own, make a list of your daily habits.
Here’s a sample of where your list might start:
Wake up
Turn off alarm
Check my phone
Go to the bathroom
Weigh myself
Take a shower
Brush my teeth
Floss my teeth
Put on deodorant
Hang up towel to dry
Get dressed
Make a cup of tea . . . and so on.
Once you have a full list, look at each behavior, and ask yourself, “Is this a good habit, a bad habit, or a neutral habit?” If it is a good habit, write “+” next to it. If it is a bad habit, write “–”. If it is a neutral habit, write “=”.For example, the list above might look like this:
Wake up =
Turn off alarm =
Check my phone –
Go to the bathroom =
Weigh myself +
Take a shower +
Brush my teeth +
Floss my teeth +
Put on deodorant +
Hang up towel to dry =
Get dressed =
Make a cup of tea +
The marks you give to a particular habit will depend on your situation and your goals. For someone who is trying to lose weight, eating a bagel with peanut butter every morning might be a bad habit.
For someone who is trying to bulk up and add muscle, the same behavior might be a good habit. It all depends on what you’re working toward.* Scoring your habits can be a bit more complex for another reason as well. The labels “good habit” and “bad habit” are slightly inaccurate.
There are no good habits or bad habits. There are only effective habits.
That is, effective at solving problems. All habits serve you in some way —even the bad ones—which is why you repeat them. For this exercise, categorize your habits by how they will benefit you in the long run.
Generally speaking, good habits will have net positive outcomes. Bad habits have net negative outcomes. Smoking a cigarette may reduce stress right now (that’s how it’s serving you), but it’s not a healthy long-term behavior.
If you’re still having trouble determining how to rate a particular habit, here is a question I like to use: “Does this behavior help me become the type of person I wish to be? Does this habit cast a vote foror against my desired identity?” Habits that reinforce your desired identity are usually good. Habits that conflict with your desired identity are usually bad.
As you create your Habits Scorecard, there is no need to change anything at first. The goal is to simply notice what is actually going on.
Observe your thoughts and actions without judgment or internal criticism. Don’t blame yourself for your faults. Don’t praise yourself for your successes.
If you eat a chocolate bar every morning, acknowledge it, almost as if you were watching someone else. Oh, how interesting that they would do such a thing. If you binge-eat, simply notice that you are eating more calories than you should. If you waste time online, notice that you are spending your life in a way that you do not want to.
The first step to changing bad habits is to be on the lookout for them. If you feel like you need extra help, then you can try Pointing- and-Calling in your own life. Say out loud the action that you are thinking of taking and what the outcome will be. If you want to cut back on your junk food habit but notice yourself grabbing another cookie, say out loud, “I’m about to eat this cookie, but I don’t need it.
Eating it will cause me to gain weight and hurt my health.”
Hearing your bad habits spoken aloud makes the consequences seem more real. It adds weight to the action rather than letting yourself mindlessly slip into an old routine. This approach is useful even if you’re simply trying to remember a task on your to-do list. Just saying out loud, “Tomorrow, I need to go to the post office after lunch,” increases the odds that you’ll actually do it. You’re getting yourself to acknowledge the need for action—and that can make all the difference.
The process of behavior change always starts with awareness.
Strategies like Pointing-and-Calling and the Habits Scorecard are focused on getting you to recognize your habits and acknowledge the cues that trigger them, which makes it possible to respond in a way that benefits you.
Key Points
# With enough practice, your brain will pick up on the cues that predict certain outcomes without consciously thinking about it.
# Once our habits become automatic, we stop paying attention to what we are doing.
# The process of behavior change always starts with awareness. You need to be aware of your habits before you can change them.
# Pointing-and-Calling raises your level of awareness from a nonconscious habit to a more conscious level by verbalizing your actions.
# The Habits Scorecard is a simple exercise you can use to become more aware of your behavior.
For this article we went through the following research paper:
Estimating the Contamination Factor's Distribution in Unsupervised Anomaly Detection
Lorenzo Perini . Paul-Christian B¨urkner . Arto Klami
All of the code and data is available to download from this link:
Download Code and Data
Here are some highlights from the paper:
1. Introduction
...
Therefore, we are the first to study the estimation of the contamination factor from a Bayesian perspective. We propose γGMM, the first algorithm for estimating the contamination factor's (posterior) distribution in unlabeled anomaly detection setups. First, we use a set of unsupervised anomaly detectors to assign anomaly scores for all samples and use these scores as a new representation of the data. Second, we fit a Bayesian Gaussian Mixture model with a Dirichlet Process prior (DPGMM) (Ferguson, 1973; Rasmussen, 1999) in this new space. If we knew which components contain the anomalies, we could derive the contamination factor's posterior distribution as the distribution of the sum of such components' weights. Because we do not know this, as a third step γGMM estimates the probability that the k most extreme components are jointly anomalous, and uses this information to construct the desired posterior. The method explained in detail in Section 3.
...
3. Methodology
We tackle the problem: Given an unlabeled dataset D and a set of M unsupervised anomaly detectors; Estimate a (posterior) distribution of the contamination factor γ. Learning from an unlabeled dataset has three key challenges.
First, the absence of labels forces us to make relatively strong assumptions.
Second, the anomaly detectors rely on different heuristics that may or may not hold, and their performance can hence vary significantly across datasets.
Third, we need to be careful in introducing user-specified hyperparameters, because setting them properly may be as hard as directly specifying the contamination factor.
In this paper, we propose γGMM, a novel Bayesian approach that estimates the contamination factor's posterior distribution in four steps, which are illustrated in Figure 1:
Step 1. Because anomalies may not follow any particular pattern in covariate space, γGMM maps the covariates X ∈ Rd into an M dimensional anomaly space, where the dimensions correspond to the anomaly scores assigned by the M unsupervised anomaly detectors. Within each dimension of such a space, the evident pattern is that “the higher the more anomalous”.
Step 2. We model the data points in the new space RM using a Dirichlet Process Gaussian Mixture Model (DPGMM) (Neal, 1992; Rasmussen, 1999). We assume that each of the (potentially many) mixture components contains either only normals or only anomalies. If we knew which components contained anomalies, we could then easily derive γ's posterior as the sum of the mixing proportions π of the anomalous components. However, such information is not available in our setting.
Step 3. Thus, we order the components in decreasing order, and we estimate the probability of the largest k components being anomalous.
This poses three challenges:
(a) how to represent each M -dimensional component by a single value to sort them from the most to the least anomalous,
(b) how to compute the probability that the kth component is anomalous given that the (k − 1)th is such,
(c) how to derive the target probability that k components are jointly anomalous.
Step 4. γGMM estimates the contamination factor's posterior by exploiting such a joint probability and the components' mixing proportions posterior.
A Simplified Implementation of The Above Algorithm
1. We have our dataset consisting of page views for our blog on Blogger. We load this dataset using Pandas.
2. We initialize two Unsupervised Anomaly Detection models namely:
- IsolationForest
- LocalOutlierFactor
Both of them are available in Scikit-Learn.
3. To begin with, we initialize them with the default values for hyperparameters as in the code below:
clf = IsolationForest(random_state=0).fit(X)
clf = LocalOutlierFactor().fit(X)
That means at this point the model's contamination factor is set to 'auto'.
4. Since we two models here so M = 2 for us. If there were three models, then M would be 3.
5. We get the anomaly scores:
anomalyscores_if = clf.decision_function(X)
anomalyscores_lof = clf.negative_outlier_factor_
6. For a simplified view, we plot this 2D data in a scatter plot.
import matplotlib.pyplot as plt
x = anomalyscores_if
y = anomalyscores_lof
plt.scatter(x, y)
plt.show()
7. Next, we use Bayesian Gaussian Mixture model to cluster the data of anomaly scores into two groups (one being anomalous, other being normal).
8. Next, we find the percentage of anomalous points (Class: 1).
This percentage is our contamination factor.
9. Using the above contamination factor for IsolationForest model, we find out anomalies as shown below in red:
