Summary of Reversal Of Thought: Enhancing Large Language Models with Preference-guided Reverse Reasoning Warm-up, by Jiahao Yuan et al.
Reversal of Thought: Enhancing Large Language Models with Preference-Guided Reverse Reasoning Warm-up
by Jiahao Yuan, Dehui Du, Hao Zhang, Zixiang Di, Usman Naseem
First submitted to arxiv on: 16 Oct 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 proposes a plug-and-play framework called Reversal of Thought (RoT) to enhance the logical reasoning abilities of large language models (LLMs). The existing methods for improving LLMs’ logical capabilities either increase computational costs or reduce flexibility. RoT uses a Preference-Guided Reverse Reasoning warm-up strategy that integrates logical symbols and meta-cognitive mechanisms to generate task-specific prompts through demonstrations, aligning with LLMs’ cognitive preferences shaped by reinforcement learning from human feedback. The framework assesses knowledge boundaries and expands LLMs’ reasoning capabilities by aggregating solution logic for known tasks and stylistic templates for unknown tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps big language models do math and logical thinking better. Right now, these models are good at understanding what people say, but they’re not great at doing complex calculations or logical puzzles. Some ways to improve their reasoning skills make them work harder or limit how flexible they can be. This new approach, called Reversal of Thought (RoT), is a simple and efficient way to help language models do better logic and math problems. |
Keywords
» Artificial intelligence » Reinforcement learning from human feedback
