Papers with ThinkEdit

1 papers
ThinkEdit: Interpretable Weight Editing to Mitigate Overly Short Thinking in Reasoning Models (2025.emnlp-main)

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Challenge: Recent studies have shown that Large Language Models (LLMs) augmented with chain-of-thought (CoT) reasoning demonstrate impressive problem-solving abilities.
Approach: They propose a weight-editing approach to reduce overly short reasoning by steering the model along a linear direction in the representation space.
Outcome: The proposed model reduces overly short reasoning and yields significant accuracy gains on multiple math benchmarks.

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