让大模型跨不同问题复用解法，突破相似性限制。
Cross-Question Method Reuse in Large Language Models: From Word-Level Prediction to Rational Logical-Layer Reasoning
- 分离问题与解法，引导模型专注迁移解法而非识别问题
- 在广义-狭义、部分特征重合等问题上提升解法复用率
- 适合需要跨场景推理的智能问答系统使用
大语言模型广泛用于辅助解答各类问题。已有方法将解法表示为问题与对应解答的配对，实现方法复用，但通常要求问题高度相似。本文将方法复用扩展至低相似度或隐含相似性的问题。针对广义-狭义关系的问题，先分离问题与解法，再引导模型适配解法到新相关问题，使其聚焦于解法迁移而非问题识别。进一步拓展至仅共享部分特征或隐藏特征的情况，突破传统相似性约束。实验表明，该范围扩展方法提升了可复用解法的筛选概率，显著增强了跨问题方法复用的有效性。
原文摘要 · Abstract (English)
Large language models (LLMs) have been widely applied to assist in finding solutions for diverse questions. Prior work has proposed representing a method as a pair of a question and its corresponding solution, enabling method reuse. However, existing approaches typically require the questions to be highly similar. In this paper, we extend the scope of method reuse to address questions with low similarity or with hidden similarities that are not explicitly observable. For questions that are similar in a general-specific sense (i.e., broader or narrower in scope), we propose to first separate the question and solution, rather than directly feeding the pair to the LLM. The LLM is then guided to adapt the solution to new but related questions, allowing it to focus on solution transfer rather than question recognition. Furthermore, we extend this approach to cases where questions only share partial features or hidden characteristics. This enables cross-question method reuse beyond conventional similarity constraints. Experimental verification shows that our scope-extension approach increases the probability of filtering out reusable solutions, thereby improving the effectiveness of cross-question method reuse.
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