Summary of Mechanics Of Next Token Prediction with Self-attention, by Yingcong Li et al.
Mechanics of Next Token Prediction with Self-Attention
by Yingcong Li, Yixiao Huang, M. Emrullah Ildiz, Ankit Singh Rawat, Samet Oymak
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Optimization and Control (math.OC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Transformer-based language models, trained on large datasets for next-token prediction, have led to significant advances in natural language processing. This paper investigates what a single self-attention layer learns from this training objective. It is shown that training self-attention with gradient descent leads to an automaton that generates the next token through two steps: hard retrieval and soft composition. Hard retrieval involves precisely selecting high-priority input tokens associated with the last input token, while soft composition creates a convex combination of these tokens for sampling the next token. The paper rigorously characterizes these mechanics using a directed graph over tokens extracted from training data, demonstrating that gradient descent implicitly discovers strongly-connected components and self-attention learns to retrieve tokens belonging to the highest-priority SCC available in the context window. This theory relies on decomposing model weights into directional and finite components corresponding to hard retrieval and soft composition steps respectively. The findings shed light on how self-attention processes sequential data, paving the way for demystifying more complex architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores what a single part of a language model learns from its training data. It shows that this part, called self-attention, works by first selecting important words related to the last word in the sentence, and then combining these words to predict the next word. The researchers use a graph-like structure to understand how this process works, showing that it’s like finding a path through a network of words. This helps us better understand how language models work and might even help us create new, more powerful models in the future. |
Keywords
* Artificial intelligence * Context window * Gradient descent * Language model * Natural language processing * Self attention * Token * Transformer
