Summary of Toward Adaptive Reasoning in Large Language Models with Thought Rollback, by Sijia Chen et al.
Toward Adaptive Reasoning in Large Language Models with Thought Rollback
by Sijia Chen, Baochun Li
First submitted to arxiv on: 27 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 A novel approach to large language model (LLM) reasoning is proposed, aiming to address limitations in step-by-step reasoning processes. The existing structure of intermediate reasoning steps is rigid and unidirectional, leading to inflexible and forward-only reasoning that may result in false responses, or “hallucinations.” This paper introduces Thought Rollback (TR), a framework allowing LLMs to adaptively build thought structures while maintaining effective problem-solving under “hallucinations.” The core mechanism of TR involves rolling back thoughts for error analysis and revision. By including trial-and-error prompts, each rollback leads to a more reliable reasoning path. Experimental results demonstrate state-of-the-art performance on mathematical problems and multi-task reasoning with GPT-4, outperforming the current best by 9% on the MATH dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart computers that can solve many tasks just like we do, one step at a time. But sometimes they make mistakes or get stuck. This paper shows how to fix this problem by letting the computer “try again” and learn from its mistakes. It’s like when you’re trying to solve a math problem and you need to start over because you made a mistake. The new way of thinking is called Thought Rollback, and it helps the computer be more accurate and efficient. |
Keywords
» Artificial intelligence » Gpt » Large language model » Multi task
