DynaThink: Fast or Slow? A Dynamic Decision-Making Framework for Large Language Models
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911938825420800 |
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| author | Pan, Jiabao Zhang, Yan Zhang, Chen Liu, Zuozhu Wang, Hongwei Li, Haizhou |
| author_facet | Pan, Jiabao Zhang, Yan Zhang, Chen Liu, Zuozhu Wang, Hongwei Li, Haizhou |
| contents | Large language models (LLMs) have demonstrated emergent capabilities across diverse reasoning tasks via popular Chains-of-Thought (COT) prompting. However, such a simple and fast COT approach often encounters limitations in dealing with complicated problems, while a thorough method, which considers multiple reasoning pathways and verifies each step carefully, results in slower inference. This paper addresses the challenge of enabling LLMs to autonomously select between fast and slow inference methods, thereby optimizing both efficiency and effectiveness. We introduce a dynamic decision-making framework that categorizes tasks into two distinct pathways: 'Fast', designated for tasks where the LLM quickly identifies a high-confidence solution, and 'Slow', allocated for tasks that the LLM perceives as complex and for which it has low confidence in immediate solutions as well as requiring more reasoning paths to verify. Experiments on five popular reasoning benchmarks demonstrated the superiority of the DynaThink over baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_01009 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DynaThink: Fast or Slow? A Dynamic Decision-Making Framework for Large Language Models Pan, Jiabao Zhang, Yan Zhang, Chen Liu, Zuozhu Wang, Hongwei Li, Haizhou Computation and Language Large language models (LLMs) have demonstrated emergent capabilities across diverse reasoning tasks via popular Chains-of-Thought (COT) prompting. However, such a simple and fast COT approach often encounters limitations in dealing with complicated problems, while a thorough method, which considers multiple reasoning pathways and verifies each step carefully, results in slower inference. This paper addresses the challenge of enabling LLMs to autonomously select between fast and slow inference methods, thereby optimizing both efficiency and effectiveness. We introduce a dynamic decision-making framework that categorizes tasks into two distinct pathways: 'Fast', designated for tasks where the LLM quickly identifies a high-confidence solution, and 'Slow', allocated for tasks that the LLM perceives as complex and for which it has low confidence in immediate solutions as well as requiring more reasoning paths to verify. Experiments on five popular reasoning benchmarks demonstrated the superiority of the DynaThink over baselines. |
| title | DynaThink: Fast or Slow? A Dynamic Decision-Making Framework for Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2407.01009 |