PART 2 — The “Attention-Based” Model Chronicles: A Comedic Breakdown

you can refer to my other two articles in this series to gain a better understanding and grasp of this world of GAI

1. PART 1 — The First GAI-LLM: Original “Transformer” Embodied the Foundational Concepts of Modern GAI(LLM/LVM/LAM/LTMs), RAG, Prompt Tuning, and More , link :https://medium.com/@trishulchowdhury.23/part-1-the-first-gai-llm-original-transformer-embodied-the-foundational-concepts-of-modern-d3f8dea1f027
2. PART 3 — Decoding Attention: The Magic Behind Generative & Contextual AI Models (Transformer, Llama, GPT, BERT, etc.),link :https://medium.com/@trishulchowdhury.23/part-3-decoding-attention-the-magic-behind-generative-contextual-ai-models-transformer-8dc92d35d8e0

Content:

🌟 Understand the difference between Generative AI (GAI) and Contextual AI (CAI)

🤔 LLM can be both GAI & CAI — Don’t be confused

✅ Is BERT an LLM? Short Answer: Yes

🧩 The First Architecture: Transformer (GAI & CAI)

• 📊 A Quick Breakdown of Attention-Based Models:
• ✨🔮 Revolutionary Design: The Original Transformer — The OG Sequence Sorcerer
• 📝🔧 Feature Extraction Masters: Encoder-Only Models (Auto Encoder) — The Text Whisperers
• 🪄🔡 Sequential Prediction Experts: Decoder-Only Models (Auto Regressor) — The Token Wizards
• 🔄🤖 Versatile Translators: Sequence-to-Sequence (Seq2Seq) Models — The Dynamic Duo of Transformers

🧠📈 Conclusion: The Evolution of Attention Mechanism in NLP — Drawing a parallel analogy with classical ML

🚀 Future of Language Models

❓ An intuitive question to make the understanding more clear:

🗨️ BERT’s Role in Chatbots?

Ready, Set, Go! Let’s Dive In!

All generative LLMs are part of generative AI, but not all generative AI models are LLMs. LLMs can be either generative or focused on understanding, depending on their architecture and training tasks. This distinction helps in choosing the right model for specific applications, whether it’s for generating human-like text or understanding and analyzing language. For instance, BERT is an LLM but it is not generative in nature; it rather focuses on understanding the context of the language.

Is BERT an LLM?

Short Answer: Yes, BERT (Bidirectional Encoder Representations from Transformers) is a type of Language Model (LM) and falls under the broader category of Large Language Models (LLMs).

The first Transformer architecture was introduced in the seminal paper titled “Attention is All You Need” by Vaswani et al. in June 2017. This groundbreaking work presented the Transformer model, which relies entirely on self-attention mechanisms, without using recurrent or convolutional layers, for natural language processing tasks. The publication of this paper marked a significant milestone in the field of deep learning and natural language processing.

Understanding the “Attention is All You Need” Paper

The main “Attention is All You Need” paper can be thought of as introducing a mathematical or statistical tool that helps understand and process the context of natural language. Using this tool (the attention mechanism with encoder-decoder architecture, sometimes with only an encoder, sometimes with only a decoder, etc.), the research community has built various models.

The First Architecture: Transformer

Transformer (Both Encoder-Decoder): Initially trained on the “Translation Task.” The encoder and decoder are not trained using Masked Language Modeling (MLM) or Next Sentence Prediction (NSP) like BERT . Instead, they are trained together in a supervised manner specifically for the task of translation. (Don’t get confused with BERT’s pre-trained task of Masked Language Modeling (MLM) and the Transformer’s decoder’s masked attention mechanism.)

• Main Training Task: Language Translation
• Encoder: Trained to process and encode the source sentence.
• Decoder: Trained to generate the target sentence based on the encoder’s output and the partially generated target sentence.
• Training Data: Paired source-target sentences (parallel corpus)

The attention mechanism introduced by the original Transformer has been a pivotal tool in NLP, replicated and expanded upon by models like BERT, GPT, and LLaMA2. Each of these models has molded the foundational concept of attention to innovate in unique ways, enhancing their ability to understand and generate language more effectively. This continuous evolution underscores the transformative impact of the attention mechanism on modern NLP.

A few key points to remember to avoid confusion:

• Algorithm: Attention mechanism — Algorithm (common for all).
• Model/Architecture Variation: Transformers, BERT, GPT, LLaMA2, etc.
• Training Task: Transformer (language translation), BERT (NSP, MLM), GPT, LLaMA (CLM), etc.

A Quick Breakdown of Attention-Based Models

Created by the Author

Let’s quickly review the “Attention-Based” Model Chronicles

1. ✨🔮 Revolutionary Design : The Original Transformer — The OG Sequence Sorcerer

Purpose: Designed for sequence-to-sequence tasks such as machine translation.

• Examples: The model described in the “Attention is All You Need” paper.

Characteristics:

• Encoder: Multiple layers of self-attention and feedforward networks, encoding the input sequence into contextualized representations.
• Decoder: Multiple layers of self-attention and feedforward networks, generating the output sequence by attending to previous outputs and the encoder’s representations.
• Training: Utilizes teacher forcing, training on pairs of input and output sequences.
• Output: Generates the output sequence one token at a time, using the encoder’s output as context.
• Pre-training Tasks: Typically trained directly on translation tasks.

2. 📝🔧 Feature Extraction Masters : Encoder-Only Models (Auto Encoder) - The Text Whisperers

Purpose: Designed for understanding and representation of input text, excelling at tasks like text classification, named entity recognition, and sentiment analysis.

• Examples: BERT, RoBERTa, DistilBERT.

Characteristics:

• Architecture: Multiple layers of self-attention and feedforward networks.
• Training: Uses tasks like Masked Language Modeling (MLM) to predict masked tokens.
• Output: Produces a contextualized representation of the entire input sequence.
• Pre-training Tasks:
• MLM: Predicts randomly masked tokens in the input sequence.
• NSP: Predicts if a given sentence follows another sentence in the text (used in BERT, but not in all encoder-only models).

3. 🪄🔡 Sequential Prediction Experts: Decoder-Only Models (Auto Regressor) - The Token Wizards

Purpose: Designed for tasks requiring text generation, such as language modeling and completing partial sequences.

• Examples: GPT Family, LLaMA, XLNet, Megatron-LM.
• Characteristics:
• Architecture: Multiple layers of self-attention and feedforward networks, with masked self-attention to prevent attending to future tokens.
• Training: Uses autoregressive language modeling to predict the next token based on previous tokens.
• Output: Generates text one token at a time, using previously generated tokens as context.
• Pre-training Tasks:
• Autoregressive Language Modeling: Predicts the next token in a sequence, given the previous tokens.

4 . 🔄🤖 Versatile Translators: Sequence-to-Sequence (Seq2Seq) Models - The Dynamic Duo of Transformers

Purpose: Designed for tasks where an input sequence is transformed into an output sequence, such as machine translation, text summarization, and speech recognition.

• Examples: Original Transformer model, T5, BART, MarianMT.

Characteristics:

• Architecture: Combines both an encoder and a decoder. The encoder processes the input sequence to produce contextualized embeddings, which the decoder uses to generate the output sequence.
• Training: Typically uses teacher forcing during training.
• Output: The decoder generates the output sequence token by token, attending to the encoder’s output for context.

Pre-training Tasks:

• Text-To-Text Transfer: Converts all tasks into a text-to-text format, pre-trained on a mixture of unsupervised and supervised tasks (specific to T5).
• Span Corruption (T5): Replaces spans of text with a mask and the model learns to predict the missing text.

Conclusion: The Evolution of Attention Mechanism in NLP — Drawing a parallel analogy with classical ML

Creaated by DALL-E

Encoder-only models, like BERT, focus on understanding input sequences with tasks such as masked language modeling (MLM) and next sentence prediction (NSP). Decoder-only models, such as GPT, generate sequences based on previous tokens using autoregressive language modeling. Seq2Seq models combine both encoder and decoder components to transform input sequences into output sequences, exemplified by the original Transformer and T5, which use text-to-text transfer and span corruption for pre-training. The original Transformer, introduced for tasks like machine translation, employs self-attention mechanisms in both encoder and decoder, typically trained directly on translation tasks.

To make it more understandable:

• Algorithm: Linear Regression
• Example Model: Simple linear regression model predicting house prices based on square footage.
• Task: Predicting house prices given features like square footage, number of bedrooms, and location.

By drawing parallels to simpler models like linear regression, the complex world of attention mechanisms and their applications in modern NLP becomes more approachable and understandable.

Is BERT an LLM? A Deep Dive into BERT’s Capabilities

Short Answer: Yes, BERT (Bidirectional Encoder Representations from Transformers) is a type of Language Model (LM) and falls under the broader category of Large Language Models (LLMs).

• 🌐🔍 Understanding Over Generation: BERT excels at understanding the context of language using an innovative attention algorithm. Unlike models focused on generating text, BERT’s strength lies in its ability to comprehend and interpret language.
• 📊⚙️ Large-Scale Parameters: Designed with a large number of parameters, BERT learns rich representations of language. For instance, BERT-base has 110 million parameters, and BERT-large has 340 million parameters, fitting the criteria of a “large” model.
• 📚🔄 Extensive Pre-Training: Pre-trained on vast amounts of text data from the BooksCorpus and English Wikipedia, BERT captures a wide array of linguistic patterns and knowledge, making it a robust model for various NLP tasks.
• 🔁🧠 Bidirectional Contextual Embeddings: Provides deep, contextual embeddings for words in a sentence by considering both the left and right context simultaneously (bidirectional). This sophisticated context understanding is a hallmark of LLMs.
• 🛠️🎯 Versatility in Downstream Tasks: Designed to be fine-tuned on various downstream tasks such as text classification, named entity recognition, and question answering, demonstrating its versatility as an LLM.
• 🌟📈 Impact on NLP: BERT has significantly impacted the field of Natural Language Processing (NLP), becoming a foundational model that has inspired many other large-scale models and applications.

Summary

BERT is indeed a Large Language Model (LLM) due to its large-scale architecture, extensive pre-training, and ability to generate rich, contextualized word embeddings suitable for a variety of NLP tasks. While BERT, GPT, and LLaMA2 are leading LLMs with distinct features, BERT specializes in text comprehension, GPT excels in text creation, and LLaMA2 balances both.

Comparing BERT, GPT, and LLaMA2

• BERT: Uses an encoder-only, bidirectional approach for deep contextual understanding, excelling in text classification and question answering.
• GPT: A decoder-only model that predicts the next word in a sequence, making it exceptional for text generation and dialogue systems.
• LLaMA2: Combines encoder and decoder elements, offering versatility for a wide range of NLP tasks.

Future of Language Models

With models like GPT and LLaMA3.1 having a significantly higher number of parameters, BERT might be considered a Small Language Model (SLM) in comparison. However, as we progress, we may encounter even larger models, potentially termed as Giant Language Models (GLM) or Very Large Language Models (VLLM)

An intuitive question to make the understanding more clear:

BERT’s Role in Chatbots?

Although BERT is not primarily designed for generating text, it excels in understanding and representing text for various NLP tasks. However, it can be part of a chatbot system in the following ways:

• Understanding User Input: BERT interprets the user’s input, identifies intents, and manages context.

Generating Responses:

• Pre-defined responses: BERT maps user input to pre-defined answers.
• Response selection: BERT ranks candidate responses.

Hybrid approach: BERT for understanding and context, GPT for generating responses.

By combining BERT’s strong understanding capabilities with a generative model like GPT, you can build an effective chatbot that leverages the strengths of both models.

In essence, all generative LLMs are part of generative AI, but not all generative AI models are LLMs. LLMs can be either generative or focused on understanding, depending on their architecture and training tasks. This distinction helps in choosing the right model for specific applications, whether it’s for generating human-like text or understanding and analyzing language.

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