Language Model History — Before and After Transformer: The AI Revolution

Language Model History

Introduction

Language models are a type of artificial intelligence (AI) that is trained to understand and generate human language. They are used in a variety of applications, including machine translation, text summarization, question answering, and chatbots.

The Transformer architecture is a neural network architecture that was first introduced in 2016. It has revolutionized the field of natural language processing (NLP) and is now the core architecture of many state-of-the-art language models.

1. Early Days: Word2Vec, N-grams, RNN, LSTM

Before the Transformer architecture, language models were typically based on recurrent neural networks (RNNs). RNNs are good at learning long-range dependencies in sequential data, such as text. However, they can be slow to train and prone to overfitting. Especially, the early-day LM does not have the ability to recognize the context of words in a long sequence.

Some examples of language models that were developed before Transformer include:

• Word2vec embeddings can be used to improve the performance of language models on a variety of tasks, such as machine translation, text summarization, and question answering. For example, a machine translation model can use word2vec embeddings to learn the relationships between words in different languages.
• N-grams are sequences of n consecutive words or characters. N-grams are often used in NLP tasks such as language modeling and machine translation. For example, a language model can use n-grams to predict the next word in a sentence. A machine translation model can use n-grams to translate phrases or idioms.
• RNNs (recurrent neural networks) are a type of neural network that are well-suited for processing sequential data, such as text. RNNs can learn long-range dependencies in the sequence, which is important for NLP tasks such as machine translation and question answering.
• LSTMs (long short-term memory networks) are a type of RNN that are particularly well-suited for learning long-range dependencies. LSTMs have been shown to achieve state-of-the-art results on a variety of NLP tasks, such as machine translation and question answering.

Word2vec, N-grams, RNN, and LSTM are all natural language processing (NLP) techniques that were developed before the Transformer architecture. However, they are still widely used today, either on their own or in conjunction with the Transformer architecture.

For example, Word2vec embeddings are often used to initialize the word embeddings in Transformer models. N-grams are often used in conjunction with Transformer models for tasks such as machine translation and question answering. RNNs and LSTMs are often used in conjunction with Transformer models for tasks such as text summarization and natural language inference.

2. The Transformer Era (2016)

The game-changer arrived in 2016 with the introduction of the Transformer architecture. This marked a shift from sequential to self-attention mechanisms, allowing models to consider all words in a sequence simultaneously. The Transformer laid the groundwork for subsequent groundbreaking models.

I will write another post to help you understand the Transformer architecture in the easiest way or in layman's terms as much as possible as I know many of us are struggling to read research papers or comprehend them completely.

a. BERT (2018): Bidirectional Contextual Embeddings

BERT revolutionized language understanding by introducing bidirectional context to capture deeper semantic relationships in text. We’ll explore how BERT’s architecture differs from its predecessors and paved the way for more contextually aware models.

b. T5 (2019): Text-to-Text Transfer Transformer

Building on the Transformer architecture, T5 took a step further by framing all NLP tasks as a text-to-text problem. This unified approach showcased the model’s versatility and applicability across various language tasks.

c. GPT-3 (2020): Language Generation at its Pinnacle

Enter the era of massive-scale language models with GPT-3. We’ll delve into its impressive capabilities, exploring how its sheer size and diverse training data enable it to perform tasks ranging from text completion to language translation.

d. PaLM (2022): Pushing the Boundaries of Contextual Understanding

PaLM, or Progressive Language Model, continued the trajectory of improvements in contextual understanding. We’ll dissect the advancements that set PaLM apart from its predecessors and its impact on real-world applications.

e. Bard (2023): The Next Frontier

The journey culminates with Bard, the latest addition to the family of language models. We’ll unravel the unique features and improvements that Bard brings to the table, pushing the boundaries of what language models can achieve in 2023. In this year, we also witness the invention of some LLM like GPT-4, Wu Dao 2.0, Bloom, Megatron-Turing NLG 530B.

3. Comparative Analysis: BERT vs T5 vs GPT-3 vs PaLM vs Bard

a. Comparison Table

The table below is a comprehensive overview of the key features of BERT, T5, GPT-3, PaLM, and Bard. It highlights the architectural differences between these models and their respective strengths and limitations.

As we know, Transformer has 3 key components:

• Encoder
• Decoder,
• Self-attention machenism (The hero)

And take a look at the table, we can see that all models are based on transformer architecture with a little of modification on Encoder, Decoder to suit different purposes in NLP. And that’s how we have many LLM right now.

Here’s a more detailed elaboration on the observations:

1. Transformer architecture as the foundation: The Transformer architecture, introduced in 2016, has become the cornerstone for many modern LLMs. Its unique self-attention mechanism allows the model to capture long-range dependencies in text, enabling it to perform a wide range of NLP tasks with remarkable accuracy.
2. Modifications to encoder and decoder: The fundamental Transformer architecture can be adapted to suit different NLP tasks by making modifications to the encoder and decoder components. For instance, BERT employs a bidirectional encoder to process input in both directions, while GPT-3 utilizes a unidirectional encoder for efficient autoregressive generation.
3. Diversity of LLMs: The adaptability of the Transformer architecture has led to the emergence of a diverse range of LLMs, each optimized for specific NLP applications. BERT excels at natural language understanding tasks, T5 shines in text-to-text tasks, GPT-3 demonstrates exceptional text generation capabilities, PaLM boasts progressive learning and enhanced context, and Bard showcases dynamic conversational AI abilities.

b. Encoder difference

Here is an explanation of the difference between bidirectional and unidirectional encoders in the context of language models:

Bidirectional encoders process the input sequence in both directions, allowing them to capture long-range dependencies in the context of the entire sentence. This is particularly useful for tasks such as machine translation and natural language inference, where understanding the relationships between words across the entire sentence is crucial.

Unidirectional encoders, on the other hand, process the input sequence from left to right or right to left, only considering the context of the words that have already been processed. This makes them less capable of capturing long-range dependencies but can be more efficient in terms of computation. Unidirectional encoders are often used in autoregressive models, where the next word in the output sequence is generated based on the previously generated words and the context of the input sequence.

In the context of the table you provided, BERT and PaLM utilize bidirectional encoders, allowing them to capture bidirectional context and excel at tasks that require understanding long-range dependencies. GPT-3, on the other hand, employs a unidirectional encoder, making it more efficient but potentially less effective at capturing long-range context.

Here’s a summary of the key differences between bidirectional and unidirectional encoders:

The choice between a bidirectional or unidirectional encoder depends on the specific task and the desired trade-off between contextual understanding and computational efficiency. For tasks that require understanding long-range dependencies, bidirectional encoders are generally preferred. However, for autoregressive tasks where efficiency is crucial, unidirectional encoders may be a better choice.

b. Decoder difference

Here is an explanation of the difference between autoregressive and progressive decoders in the context of language models:

Autoregressive decoders generate the output sequence one word at a time, predicting the next word based on the previously generated words and the context of the input sequence. This approach is particularly well-suited for tasks such as text generation and machine translation, where the order of the words in the output sequence is important.

Progressive decoders, on the other hand, generate the output sequence in a more incremental manner, predicting parts of the output sequence at a time and refining their predictions as more information becomes available. This approach can lead to more fluent and coherent output, especially for tasks like summarization and question answering.

In the context of the table you provided, GPT-3, Bard, and T5 utilize autoregressive decoders, enabling them to generate text sequentially while considering the context of the input sequence. PaLM, on the other hand, employs a progressive decoder, allowing it to refine its predictions as more information becomes available, potentially leading to more fluent and coherent output.

Here’s a summary of the key differences between autoregressive and progressive decoders:

The choice between an autoregressive or progressive decoder depends on the specific task and the desired trade-off between fluency and efficiency. For tasks that require strict adherence to the order of words, autoregressive decoders are generally preferred. However, for tasks where fluency and coherence are crucial, progressive decoders may be a better choice.

That’s it for today.

Stay tunned and wait for my post explaining the transformer in the easiest way as possible soon.

Thank you and happy reading.