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REFINER: Reasoning Feedback on Intermediate Representations

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arxiv 2304.01904 v2 pith:LXCR2BOC submitted 2023-04-04 cs.CL

Debjit Paul , Mete Ismayilzada , Maxime Peyrard , Beatriz Borges , Antoine Bosselut , Robert West , Boi Faltings This is my paper
classification cs.CL
keywords reasoningintermediatecriticfeedbackrefinerexplicitlyfinetuninginference
verification ladder T0 review T1 audit T2 compute T3 formal
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Language models (LMs) have recently shown remarkable performance on reasoning tasks by explicitly generating intermediate inferences, e.g., chain-of-thought prompting. However, these intermediate inference steps may be inappropriate deductions from the initial context and lead to incorrect final predictions. Here we introduce REFINER, a framework for finetuning LMs to explicitly generate intermediate reasoning steps while interacting with a critic model that provides automated feedback on the reasoning. Specifically, the critic provides structured feedback that the reasoning LM uses to iteratively improve its intermediate arguments. Empirical evaluations of REFINER on three diverse reasoning tasks show significant improvements over baseline LMs of comparable scale. Furthermore, when using GPT-3.5 or ChatGPT as the reasoner, the trained critic significantly improves reasoning without finetuning the reasoner. Finally, our critic model is trained without expensive human-in-the-loop data but can be substituted with humans at inference time.

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Cited by 5 Pith papers

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