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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2304.01904 |
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| _version_ | 1866909091618619392 |
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| author | Paul, Debjit Ismayilzada, Mete Peyrard, Maxime Borges, Beatriz Bosselut, Antoine West, Robert Faltings, Boi |
| author_facet | Paul, Debjit Ismayilzada, Mete Peyrard, Maxime Borges, Beatriz Bosselut, Antoine West, Robert Faltings, Boi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_01904 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | REFINER: Reasoning Feedback on Intermediate Representations Paul, Debjit Ismayilzada, Mete Peyrard, Maxime Borges, Beatriz Bosselut, Antoine West, Robert Faltings, Boi Computation and Language 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. |
| title | REFINER: Reasoning Feedback on Intermediate Representations |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2304.01904 |