THOUGHTSCULPT: Reasoning with Intermediate Revision and Search
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917923801530368 |
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| author | Chi, Yizhou Yang, Kevin Klein, Dan |
| author_facet | Chi, Yizhou Yang, Kevin Klein, Dan |
| contents | We present THOUGHTSCULPT, a general reasoning and search method for tasks with outputs that can be decomposed into components. THOUGHTSCULPT explores a search tree of potential solutions using Monte Carlo Tree Search (MCTS), building solutions one action at a time and evaluating according to any domain-specific heuristic, which in practice is often simply an LLM evaluator. Critically, our action space includes revision actions: THOUGHTSCULPT may choose to revise part of its previous output rather than continuing to build the rest of its output. Empirically, THOUGHTSCULPT outperforms state-of-the-art reasoning methods across three challenging tasks: Story Outline Improvement (up to +30% interestingness), Mini-Crosswords Solving (up to +16% word success rate), and Constrained Generation (up to +10% concept coverage). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_05966 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | THOUGHTSCULPT: Reasoning with Intermediate Revision and Search Chi, Yizhou Yang, Kevin Klein, Dan Computation and Language Artificial Intelligence We present THOUGHTSCULPT, a general reasoning and search method for tasks with outputs that can be decomposed into components. THOUGHTSCULPT explores a search tree of potential solutions using Monte Carlo Tree Search (MCTS), building solutions one action at a time and evaluating according to any domain-specific heuristic, which in practice is often simply an LLM evaluator. Critically, our action space includes revision actions: THOUGHTSCULPT may choose to revise part of its previous output rather than continuing to build the rest of its output. Empirically, THOUGHTSCULPT outperforms state-of-the-art reasoning methods across three challenging tasks: Story Outline Improvement (up to +30% interestingness), Mini-Crosswords Solving (up to +16% word success rate), and Constrained Generation (up to +10% concept coverage). |
| title | THOUGHTSCULPT: Reasoning with Intermediate Revision and Search |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2404.05966 |