Large Language Models and Beyond

“Today a reader, tomorrow a leader.”

― Margaret Fuller

The Transformer is a deep learning architecture that revolutionized natural language processing (NLP) by introducing a novel way to model sequential data. Proposed by Vaswani et al. in 2017 in the paper "Attention is All You Need", the Transformer replaces traditional sequence models like recurrent neural networks (RNNs) with an entirely attention-based mechanism, enabling more efficient parallelization and capturing long-range dependencies in data.

At its core, the Transformer leverages self-attention, a mechanism that allows each element of an input sequence to attend to all other elements, weighing their importance dynamically. This enables the model to better capture contextual relationships in data, whether in text, images, or even time-series. The architecture consists of an encoder and decoder, both built from layers of self-attention and feed-forward neural networks, making it highly scalable and versatile.

Transformers have become the backbone of many state-of-the-art models in NLP, such as BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer), and have been extended beyond text to areas like vision and speech. Their ability to handle complex, multi-modal data and model long-term dependencies efficiently has made them foundational to modern AI research and applications.

Understanding TransformerS

Transformers architectures are now the basis of the most advanced systems in modeling complex data structures such as sequences. Transformers with the attention mechanism are the heart of Large Language Models such as GPT-4, Gemini, Claude. Below is a reading path that allows you to understand the intimate functioning of Transformers intuitively.

Navigating LLM Transformers in 3D

A very useful and impressive tool for understanding visually LLM Transformers

Dayly Papers on Hugging Face

What Is ChatGPT Doing … and Why Does It Work?

By Stephen Wolfram (February 14, 2023)

A highly recommended article for a basic understanding of how ChatGPT works from the perspective of complex systems.

Generative AI Space and the Mental Imagery of Alien Minds

By Stephen Wolfram (July 17, 2023)

A highly recommended article for a basic understanding of how multimodal LLMs build their representational "concept space".

Seminal Papers about Large Language Models

"Improving Language Understanding by Generative Pre-Training" by Radford et al. (2018): This is the paper that introduced the first version of the GPT model. It laid the foundation for the use of transformer-based models in natural language processing.
"Language Models are Unsupervised Multitask Learners" by Radford et al. (2019): This paper presents GPT-2, an extension of the original GPT model, with significantly more parameters and trained on a larger dataset.
"Language Models are Few-Shot Learners" by Brown et al. (2020): This paper introduces GPT-3, the third iteration in the GPT series. It highlights the model's few-shot learning capabilities, where it performs tasks with minimal task-specific data.
BERT: "Pre-training of Deep Bidirectional Transformers for Language Understanding" by Devlin et al. (2018): While not a GPT paper, this work by researchers at Google is a seminal paper in the field of LLMs. BERT introduced a new method of pre-training language representations that was revolutionary in the field.
"Attention Is All You Need" by Vaswani et al. (2017): This paper, although not directly related to GPT, is crucial as it introduced the transformer architecture, which is the backbone of models like GPT-2 and GPT-3.
"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer" by Raffel et al. (2019): This paper from Google researchers presents the T5 model, which treats every language problem as a text-to-text problem, providing a unified framework for various NLP tasks.
"XLNet: Generalized Autoregressive Pretraining for Language Understanding" by Yang et al. (2019): XLNet is another important model in the LLM domain, which outperformed BERT on several benchmarks by using a generalized autoregressive pretraining method.
"ERNIE: Enhanced Representation through Knowledge Integration" by Sun et al. (2019): Developed by Baidu, ERNIE is an LLM that integrates lexical, syntactic, and semantic information effectively, showing significant improvements over BERT in various NLP tasks.

Other seminal papers on language models and generative AI

The Utility of Large Language Models and Generative AI for Education Research: This paper explores the integration of NLP feature extraction techniques with machine learning models like SVMs and Decision Trees for educational applications like automated grading.

Science in the Age of Large Language Models: Published in Nature Reviews Physics, this article discusses the critical stage of generative AI (GenAI) in scientific research and the importance of integrating GenAI responsibly into scientific practice.

An editorial from MIT Press, "What Have Large-Language Models and Generative AI Got to Do With It?", delves into the implications of generative algorithms and the ethical use of AI-generated text in various contexts.

What ChatGPT and Generative AI Mean for Science: This Nature article provides insights into the role of ChatGPT and generative AI in the scientific community, highlighting potential impacts and considerations.

Fundamentals of Generative Large Language Models and Perspectives in Cyber-Defense: This paper discusses the text generation capabilities of LLMs, including various sampling strategies like maximum likelihood and top-K, crucial for understanding the functioning of these models.

Large Language Models for Generative Information Extraction: This survey looks at the application of LLMs in information extraction, showcasing various models and techniques in this domain.

Autonomous Chemical Research with Large Language Models: Published in Nature, this paper discusses the application of LLMs in automating chemical research, highlighting the integration of models like GPT-4 with robotic systems for laboratory task

Large Language Models and Robotics [2024]

Research Papers

Blogs

Large Language Models and Cognitive Science [2024]

Research Papers

Turning large language models into cognitive models1: This paper discusses whether large language models can be turned into cognitive models. It finds that after fine-tuning them on data from psychological experiments, these models offer accurate representations of human behavior1.
Cognitive Effects in Large Language Models2: This work tested GPT-3 on a range of cognitive effects, which are systematic patterns usually found in human cognitive tasks. It found that LLMs are indeed prone to several human cognitive effects2.
Large language models meet cognitive science: LLMs as tools, models, and participants3: This paper presents innovative research on the possible interactions between cognitive science and large language models3.

Blogs

Unpacking the Transformer: The Mechanism of Attention in Language Models

The attention mechanism, as visualized in the informative graphic, is a cornerstone in the architecture of modern language models, particularly the Transformer model introduced by Vaswani et al. in their seminal paper "Attention is All You Need." This revolutionary approach has shaped the development of large language models (LLMs) like OpenAI's GPT series, altering the landscape of natural language processing (NLP).

How Does the Attention Mechanism Work?

Step 1: Token Embedding and Initial Transformation The process begins with converting words into numerical representations known as embeddings. These embeddings capture semantic features of the words and are processed to generate three distinct vectors for each word: keys, queries, and values. These vectors are produced through linear transformation layers specific for each type (Wk, Wq, Wv).

Step 2: Scoring and Self-Attention Each word in a sentence is compared to every other word by computing a score that represents the degree of relevance or 'attention' one word should give to another. This is done by taking the dot product of the query vector of one word with the key vector of every other word, followed by a softmax operation to normalize the scores. The output is a set of attention scores that dictate how much each word should focus on every other word within the same sentence.

Step 3: Calculating the Contextual Embeddings The attention scores are used to create a weighted sum of value vectors, resulting in what we refer to as the attention output for each word. This output is a new embedding that incorporates contextual information from other relevant words in the sentence, allowing the model to understand the context in which each word is used.

Step 4: Combining Multiple Attention Heads The Transformer uses multiple sets of these operations (referred to as 'heads') in parallel, enabling the model to capture different types of relationships between words simultaneously. The outputs of all heads are then concatenated and once more linearly transformed to produce the final representation of each word.

Benefits of the Attention Mechanism in LLMs

Contextual Awareness: Unlike previous models that processed words in a fixed sequence, the attention mechanism allows each word to dynamically adjust its interpretation based on the surrounding words, leading to a much richer understanding of context.
Handling Long-range Dependencies: Traditional sequence models like RNNs and LSTMs often struggle with long-range dependencies due to their sequential nature. In contrast, attention mechanisms can easily relate words that are far apart in the text, capturing their relationships regardless of distance.
Parallel Computation: Since the attention mechanism processes all words simultaneously, it is inherently more suitable for parallel computation. This results in significant improvements in training speed and efficiency.
Flexibility and Scalability: The Transformer architecture is highly flexible and can be scaled up (as seen in models like GPT-3) to handle increasingly complex tasks and larger datasets.

References

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems (pp. 5998-6008).
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI Blog, 1(8), 9.
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. In Advances in neural information processing systems (pp. 1877-1901).