Understanding the Evolution of Language Models: From Word2Vec to BERT and Transformers

Source: Information sourced from the lecture given by Kanav Bansal Sir during the internship program.

1.Difference Between Word2vec and Bert

Word2Vec and BERT are both popular models in natural language processing (NLP), but they have significant differences in their architecture, directionality, representation, and distributed representation.

Architecture:

Word2Vec: Word2Vec is a shallow neural network model that typically comes in two architectures: Continuous Bag of Words (CBOW) and Skip-gram. CBOW predicts the current word based on the context words, while Skip-gram predicts surrounding context words given a central word.

BERT: BERT (Bidirectional Encoder Representations from Transformers) is a deep bidirectional transformer model. It consists of multiple layers of transformers, which are stacked to capture contextual relationships between words in a bidirectional manner..

Representation:

• Word2Vec: Word2Vec represents each word in a fixed-size vector space. Each word is mapped to a dense vector of continuous values (typically 100-300 dimensions). These vectors capture semantic and syntactic similarities between words.
• BERT: BERT also represents words as dense vectors, but it generates contextualized word embeddings. This means that the representation of each word depends on the entire context in which it appears in a sentence. BERT representations are more informative as they consider the surrounding words' influence on the target word's meaning.

Directionality:

• Word2Vec: Word2Vec models are inherently unidirectional. CBOW predicts the target word based on its context, and Skip-gram predicts context words given a target word. However, it does not capture bidirectional context.
• BERT: BERT is bidirectional, meaning it can understand the context from both left to right and right to left. This bidirectional understanding allows BERT to capture deeper contextual relationships within sentences.

Distributed Representation:

• Both Word2Vec and BERT use distributed representation to encode words into low-dimensional dense vectors.
• Word2Vec: In Word2Vec, distributed representations are fixed and pre-trained on large corpora. Once trained, these representations are static and do not change.
• BERT: BERT's distributed representations are contextualized and dynamically generated based on the input text. This means that the representation of a word can vary depending on its context within a sentence, allowing BERT to capture nuances and polysemy more effectively.

2.Exploring Techniques for Word2Vec Training: CBOW, Skip-gram, and Negative Sampling

Continuous Bag of Words (CBOW):

• CBOW aims to predict the target word based on its surrounding context words within a fixed window size.
• It works by summing or averaging the word vectors of the context words to predict the target word.
• For example, given the sentence "The cat sat on the ___", CBOW tries to predict the target word "mat" based on the context words "The", "cat", "sat", and "on".
• CBOW is computationally efficient and tends to work well with frequent words.

Skip-gram:

• Skip-gram, on the other hand, works in the opposite way compared to CBOW. It predicts the context words given a target word.
• For each word in a sentence, Skip-gram tries to predict the context words within a fixed window size.
• It's particularly useful for capturing semantic relationships between words and tends to perform better with smaller datasets or rare words.
• Using the same example as before, Skip-gram would predict "The", "cat", "sat", and "on" based on the target word "mat".

Skip-gram with Negative Sampling (SGNS):

• SGNS is an enhancement to the Skip-gram model designed to improve efficiency, especially in large datasets.
• Instead of predicting all context words given a target word, SGNS samples a small number of negative examples (words that don't appear in the context) to train against each positive example.
• By doing so, SGNS reduces the computational cost of training and makes the model more efficient while still preserving the ability to learn high-quality word embeddings.
• Negative sampling helps to focus on learning only a small subset of the word-context pairs, which speeds up training without significantly compromising the quality of the embeddings.

Pretraining BERT: Unveiling Bidirectional Language Understanding through MLM and NSP Techniques

BERT (Bidirectional Encoder Representations from Transformers) is trained using two main techniques: Masked Language Modeling (MLM) and Next Sentence Prediction (NSP).

Masked Language Modeling (MLM):

• In the Masked Language Modeling technique, a certain percentage of the input tokens are randomly masked out.
• BERT is then trained to predict the original vocabulary ID of the masked tokens based on the surrounding context.
• This bidirectional approach differs from traditional left-to-right or right-to-left language models, as BERT can leverage both the left and right context of each token during training.
• The objective of MLM encourages BERT to learn deep bidirectional representations of the text, capturing the context and relationships between words effectively.

Next Sentence Prediction (NSP):

• NSP is another technique used to pretrain BERT, aimed at understanding relationships between pairs of sentences.
• During training, pairs of sentences are fed into the model, and BERT is trained to predict whether the second sentence follows the first sentence in the original text.
• This task helps BERT to understand the coherence and flow of text, enabling it to capture relationships between sentences.
• By training on a large corpus of text with this objective, BERT learns to understand the nuances of language and infer logical connections between sentences.

Exploring Large Language Models: GPT-4, GPT 3.5, BERT, Gemini, and LLama 2 in Natural Language Processing

Language Modeling:

• Language modeling is a fundamental task in natural language processing (NLP) where a model is trained to predict the likelihood of a sequence of words occurring in a given context.
• The goal of language modeling is to capture the statistical properties and structure of natural language, enabling the model to generate coherent and contextually relevant text.
• Language models are typically trained on large corpora of text data and can be used for various NLP tasks such as machine translation, text generation, and speech recognition.

Large Language Modeling:

• Large language modeling refers to the development and deployment of language models that are trained on massive amounts of text data and have a high number of parameters.
• These models are capable of capturing intricate patterns and nuances of language, allowing them to generate high-quality text and perform well on a wide range of NLP tasks.
• Large language models often require substantial computational resources for training and inference due to their size and complexity.

Examples of Large Language Models

• GPT-4: Hypothetical successor to the GPT-3 series, envisioned to be an even larger and more powerful language model developed by OpenAI.
• GPT-3.5: A hypothetical iteration between GPT-3 and a potential GPT-4, designed to bridge the gap and improve upon the capabilities of GPT-3.
• BERT (Bidirectional Encoder Representations from Transformers): Although not as large as some other models, BERT is a significant example of a large language model. It was pre-trained on massive amounts of text data and has been widely adopted for various NLP tasks.
• GPT (Generative Pre-trained Transformer) series: Including GPT-2 and GPT-3, these models are developed by OpenAI and are among the largest language models available. They have been highly influential in advancing the field of NLP and are capable of generating coherent and contextually relevant text.
• GPT-4: Hypothetical successor to the GPT-3 series, envisioned to be an even larger and more powerful language model developed by OpenAI.
• BERT (Bidirectional Encoder Representations from Transformers): Although not as large as some other models, BERT is a significant example of a large language model. It was pre-trained on massive amounts of text data and has been widely adopted for various NLP tasks.
• Gemini: Developed by Microsoft, Gemini is a large-scale language model designed to improve conversational AI systems' capabilities.
• LLama 2 (Large Language Model): LLama 2 is another large language model developed by the Allen Institute for Artificial Intelligence (AI2), aiming to advance the capabilities of language understanding and generation.

Multi-Modality

• Multi-modality in transformers refers to the capability of transformer-based models to process and generate outputs based on input data from diverse modalities such as text, images, audio, etc.
• It enables transformers to handle and integrate information from different sources or types of data within a unified architecture.

How it Works in Transformers:

• Multi-modal transformers typically incorporate separate encoder networks tailored to each modality.
• These encoder networks process input data from different modalities independently, extracting relevant features specific to each modality.
• The encoded representations from each modality are then fused together, allowing the model to effectively integrate information across modalities.
• The fused representations are fed into a shared decoder network, enabling the model to generate outputs that combine information from multiple modalities.
• By processing and integrating information from diverse modalities, multi-modal transformers can perform tasks that require understanding or generation across different types of data, such as image captioning, video understanding, and more.

Unlocking the Potential of Language Models: Applications in Natural Language Processing

Building language models can achieve a wide range of tasks and applications in natural language processing (NLP). Here are some examples:

Autocomplete Features:

• Language models can be used to predict the next word or phrase in a sentence, enabling autocomplete features in text editors, search engines, messaging apps, and virtual keyboards.
• Example: Predictive text suggestions in smartphones or search engine suggestions.

Text Summarization:

• Language models can generate concise summaries of longer texts, helping users quickly understand the main points of articles, documents, or conversations.
• Example: Summarizing news articles, research papers, or meeting transcripts.

Chatbots and Conversational Agents:

• Language models can power chatbots and conversational agents that engage in natural language conversations with users, providing assistance, answering questions, or performing tasks.
• Example: Virtual assistants like Siri, Alexa, or Google Assistant, customer service chatbots on websites.

Question Answering Systems:

• Language models can answer questions posed in natural language by extracting relevant information from text sources or knowledge bases.
• Example: Providing answers to factual questions, assisting with FAQs, or helping users find information.

Language Translation:

• Language models can be used for machine translation tasks, converting text from one language to another while preserving meaning and context.
• Example: Google Translate, DeepL, or Microsoft Translator.

Sentiment Analysis:

• Language models can analyze text data to determine the sentiment or emotional tone expressed, helping businesses understand customer feedback, social media sentiment, or product reviews.
• Example: Analyzing customer reviews, social media posts, or survey responses.

Text Generation:

• Language models can generate coherent and contextually relevant text, which can be used for creative writing, content generation, or storytelling.
• Example: Generating product descriptions, marketing copy, or personalized emails.

Named Entity Recognition (NER):

• Language models can identify and classify named entities such as people, organizations, locations, dates, and numerical expressions in text.
• Example: Extracting names of people, companies, or locations from news articles or documents.

Exploring Language Modeling: Techniques and Tasks for Understanding and Generating Human Language

Modeling a language involves constructing computational models that can understand and generate human language. Here's a simplified overview of how language modeling is approached:

Tokenization:

• The first step in language modeling is tokenization, where the text data is divided into smaller units called tokens. These tokens can be words, subwords, or characters, depending on the granularity desired.

Embedding:

• Each token is then mapped to a numerical vector representation known as an embedding. Embeddings capture the semantic and syntactic properties of words or characters in a continuous vector space.

Architecture Selection:

• Next, a suitable architecture is chosen for the language model. Popular choices include recurrent neural networks (RNNs), long short-term memory networks (LSTMs), gated recurrent units (GRUs), transformers, and their variants.

Training:

• The language model is trained on a large corpus of text data using a supervised learning approach. During training, the model learns to predict the next word or token in a sequence given the preceding context.

Evaluation:

• The trained model is evaluated on held-out validation or test datasets to assess its performance in various language understanding and generation tasks.

Now, let's delve into two examples of language modeling tasks:

Auto-regression Task

• In an auto-regression task, the language model predicts the next word or token in a sequence given the preceding context.
• Example: Given the sentence "The cat sat on the ___", the model predicts the next word, which could be "mat".

Auto-encoding Task:

• In an auto-encoding task, the language model learns to reconstruct the input sequence from a corrupted or noisy version of itself.
• Example: Given the sentence "The cat sat on the mat", the model is trained to reconstruct the original sentence from a masked or shuffled version of it.

Unveiling the Training Journey of Google's BERT: Harnessing Massive Data and Computational Power for Language Understanding

Google's BERT (Bidirectional Encoder Representations from Transformers) model was trained on a massive corpus of text data using powerful computational resources. Here's an overview of how Google trained its BERT model

Data Preparation:

• Google utilized a vast amount of text data from various sources for pre-training BERT. This corpus included sources such as Wikipedia, web pages, books, news articles, and other publicly available text sources.
• The text data was tokenized into smaller units, typically words or subwords, and converted into numerical representations suitable for processing by the BERT model.

Training Setup:

• Google used large-scale distributed computing infrastructure, such as GPU clusters or TPUs (Tensor Processing Units), to train the BERT model efficiently.
• The training process involved parallelizing computations across multiple processing units to handle the massive amounts of data and computational resources required.

Model Architecture:

• BERT is based on the transformer architecture, which is well-suited for processing sequential data such as text.
• The model consists of multiple layers of transformers, each containing self-attention mechanisms and feedforward neural networks.

Tokenization and Sequence Length:

• Google used a tokenization scheme that breaks down words into smaller subword units to handle out-of-vocabulary words and increase model vocabulary coverage.
• The maximum sequence length used during training typically ranged from 128 to 512 tokens per input example, depending on the specific BERT variant and task.

Training Objective:

• BERT is pre-trained using two main objectives: Masked Language Modeling (MLM) and Next Sentence Prediction (NSP).
• The MLM objective involves randomly masking certain tokens in the input and training the model to predict the original tokens based on the context.
• The NSP objective involves predicting whether two sentences are consecutive or not, helping the model learn contextual relationships between sentences.

Training Duration:

• Training BERT on such a large corpus and with significant computational resources typically requires several days to weeks, depending on the scale of the training data, model size, and infrastructure used
• Google likely leveraged its extensive compute infrastructure to parallelize training and reduce the overall training time.
• Google utilized 3.2 billion tokens for training its own BERT model.