Thought Branches: Interpreting LLM Reasoning Requires Resampling
NeutralArtificial Intelligence
A new study published on arXiv highlights the limitations of interpreting reasoning models by focusing on a single chain-of-thought. The researchers argue that understanding the full distribution of possible reasoning paths is crucial for grasping causal influences and computational processes. By employing resampling techniques, they demonstrate how this approach can provide deeper insights into model decisions, which is significant for advancements in cognitive science and machine learning.
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