用逻辑推导增强问答，减少大模型幻觉。
Derivation Prompting: A Logic-Based Method for Improving Retrieval-Augmented Generation
- 基于逻辑推导构建可解释的推理树，控制生成过程
- 在特定任务中显著降低不可接受答案比例
- 适合需要高可信度推理的领域应用
大型语言模型在问答任务中展现出巨大潜力，但在知识密集型、领域特定任务中仍面临幻觉和错误推理等挑战。为此，本文提出一种新的提示方法——推导提示（Derivation Prompting），用于检索增强生成框架中的生成阶段。该方法受逻辑推导启发，通过系统应用预定义规则，从初始假设推导出结论，构建可解释的推导树，从而增强生成过程的可控性。我们在一个具体案例研究中验证了该方法，结果表明其显著降低了不可接受答案的比例，优于传统RAG和长上下文窗口方法。
原文摘要 · Abstract (English)
The application of Large Language Models to Question Answering has shown great promise, but important challenges such as hallucinations and erroneous reasoning arise when using these models, particularly in knowledge-intensive, domain-specific tasks. To address these issues, we introduce Derivation Prompting, a novel prompting technique for the generation step of the Retrieval-Augmented Generation framework. Inspired by logic derivations, this method involves deriving conclusions from initial hypotheses through the systematic application of predefined rules. It constructs a derivation tree that is interpretable and adds control over the generation process. We applied this method in a specific case study, significantly reducing unacceptable answers compared to traditional RAG and long-context window methods.
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