Summary of Universal Response and Emergence Of Induction in Llms, by Niclas Luick
Universal Response and Emergence of Induction in LLMs
by Niclas Luick
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 Induction is a crucial mechanism in Large Language Models (LLMs) for learning in-context. However, decomposing its precise circuit behavior beyond toy models remains challenging. This paper investigates the emergence of induction behavior within LLMs by perturbing their residual streams with single tokens. The results show that LLMs exhibit a scale-invariant response to perturbation strength changes, allowing quantification of token correlations throughout the model. By applying this method, signatures of induction are observed in Gemma-2-2B, Llama-3.2-3B, and GPT-2-XL residual streams, which gradually emerge within intermediate layers. These findings provide insights into component interactions within LLMs, serving as a benchmark for large-scale circuit analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how Large Language Models learn new information in different situations. They used special tests to see if these models could learn from small changes in the words they were given. The results show that these models can learn and remember patterns in the language, even when the changes are very small. This helps us understand how these models work and what makes them good at learning. The findings also give us a way to study the inner workings of these models more closely. |
Keywords
» Artificial intelligence » Gpt » Llama » Token
