Papers with reasoning LM

2 papers
REFINER: Reasoning Feedback on Intermediate Representations (2024.eacl-long)

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Challenge: Language models (LLMs) have shown remarkable performance by explicitly generating intermediate inferences,e.g., chain-of-thought prompting.
Approach: They propose a framework for finetuning LMs to generate intermediate reasoning steps while interacting with a critic model that provides automated feedback on the reasoning.
Outcome: Empirical evaluations of REFINER on three diverse reasoning tasks show that it significantly improves over baseline models.
Making Reasoning Matter: Measuring and Improving Faithfulness of Chain-of-Thought Reasoning (2024.findings-emnlp)

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Challenge: Large language models (LLMs) are shown to perform better when asked to reason step-by-step before generating a final answer.
Approach: They propose a framework to tailor small-sized LMs to generate correct reasoning steps and robustly reason over these steps.
Outcome: The proposed framework outperforms four competitive baselines and improves the robustness and generalization ability of the reasoning LM, yielding higher performance on out-of-distribution test sets.

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