Summary of Interpreting Context Look-ups in Transformers: Investigating Attention-mlp Interactions, by Clement Neo et al.
Interpreting Context Look-ups in Transformers: Investigating Attention-MLP Interactions
by Clement Neo, Shay B. Cohen, Fazl Barez
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 This paper delves into the inner workings of large language models (LLMs), specifically examining the interactions between attention heads and multi-layer perceptrons (MLPs) in predicting new words. The authors propose a methodology to identify next-token neurons, determine which prompts activate them, and identify upstream attention heads responsible for their activity. By generating and evaluating explanations for these attention heads, the study reveals that some recognize specific contexts relevant to predicting a token, activating downstream token-predicting neurons accordingly. This mechanism provides insight into how LLMs perform next-token prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how big language models work. It looks at how attention and multi-layer perceptrons (MLPs) work together to predict new words. The researchers came up with a way to figure out which neurons are important, what prompts make them active, and which attention heads are involved. By doing this, they found that some attention heads recognize specific contexts that help predict the next word. This is an important step in understanding how language models work and can even help us generate better text. |
Keywords
* Artificial intelligence * Attention * Token
