Summary of Thoughtsculpt: Reasoning with Intermediate Revision and Search, by Yizhou Chi et al.
THOUGHTSCULPT: Reasoning with Intermediate Revision and Search
by Yizhou Chi, Kevin Yang, Dan Klein
First submitted to arxiv on: 9 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces THOUGHTSCULPT, a novel method for general reasoning and search that excels in tasks with decomposable outputs. It leverages Monte Carlo Tree Search (MCTS) to explore a search tree of potential solutions, evaluating each step according to a domain-specific heuristic, often an LLM evaluator. A key innovation is the inclusion of revision actions, allowing THOUGHTSCULPT to revise previous output rather than starting anew. Empirical results demonstrate THOUGHTSCULPT’s superiority over state-of-the-art methods in three challenging tasks: Story Outline Improvement, Mini-Crosswords Solving, and Constrained Generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary THOUGHTSCULPT is a new way for computers to reason and solve problems. It works by trying out different solutions and choosing the best one based on how well it does. This approach lets THOUGHTSCULPT revise its previous ideas if they’re not working, rather than starting from scratch. The results show that THOUGHTSCULPT is better than other methods at tasks like creating story outlines, solving crossword puzzles, and generating text within certain rules. |
