Can Small Language Models Help Large Language Models Reason Better?: LM-Guided Chain-of-Thought
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914741144780800 |
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| author | Lee, Jooyoung Yang, Fan Tran, Thanh Hu, Qian Barut, Emre Chang, Kai-Wei Su, Chengwei |
| author_facet | Lee, Jooyoung Yang, Fan Tran, Thanh Hu, Qian Barut, Emre Chang, Kai-Wei Su, Chengwei |
| contents | We introduce a novel framework, LM-Guided CoT, that leverages a lightweight (i.e., <1B) language model (LM) for guiding a black-box large (i.e., >10B) LM in reasoning tasks. Specifically, the lightweight LM first generates a rationale for each input instance. The Frozen large LM is then prompted to predict a task output based on the rationale generated by the lightweight LM. Our approach is resource-efficient in the sense that it only requires training the lightweight LM. We optimize the model through 1) knowledge distillation and 2) reinforcement learning from rationale-oriented and task-oriented reward signals. We assess our method with multi-hop extractive question answering (QA) benchmarks, HotpotQA, and 2WikiMultiHopQA. Experimental results show that our approach outperforms all baselines regarding answer prediction accuracy. We also find that reinforcement learning helps the model to produce higher-quality rationales with improved QA performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_03414 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Can Small Language Models Help Large Language Models Reason Better?: LM-Guided Chain-of-Thought Lee, Jooyoung Yang, Fan Tran, Thanh Hu, Qian Barut, Emre Chang, Kai-Wei Su, Chengwei Computation and Language Artificial Intelligence We introduce a novel framework, LM-Guided CoT, that leverages a lightweight (i.e., <1B) language model (LM) for guiding a black-box large (i.e., >10B) LM in reasoning tasks. Specifically, the lightweight LM first generates a rationale for each input instance. The Frozen large LM is then prompted to predict a task output based on the rationale generated by the lightweight LM. Our approach is resource-efficient in the sense that it only requires training the lightweight LM. We optimize the model through 1) knowledge distillation and 2) reinforcement learning from rationale-oriented and task-oriented reward signals. We assess our method with multi-hop extractive question answering (QA) benchmarks, HotpotQA, and 2WikiMultiHopQA. Experimental results show that our approach outperforms all baselines regarding answer prediction accuracy. We also find that reinforcement learning helps the model to produce higher-quality rationales with improved QA performance. |
| title | Can Small Language Models Help Large Language Models Reason Better?: LM-Guided Chain-of-Thought |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2404.03414 |