用大模型生成答案提示，比直接找或生成上下文更准。
Exploring Hint Generation Approaches in Open-Domain Question Answering
- 让大模型生成可能答案的提示，而非直接生成上下文。
- 在三个数据集上表现优于检索和生成方法，提升准确率。
- 适合想提升开放域问答精度的研究者与工程师。
自动问答系统依赖上下文信息提供准确答案。传统方法分为基于检索（如从维基百科检索文档）和基于生成（如使用大语言模型生成上下文）。本文提出一种新方法 HINTQA，通过自动提示生成（HG）技术，让大模型生成关于潜在答案的提示，而非生成完整上下文。我们在 TriviaQA、NaturalQuestions 和 Web Questions 三个数据集上评估该方法，考察提示数量与顺序对性能的影响。结果表明，HINTQA 在所有数据集上均优于基于检索和生成的方法，且提示能比检索或生成的上下文更有效提升答案准确率。
原文摘要 · Abstract (English)
Automatic Question Answering (QA) systems rely on contextual information to provide accurate answers. Commonly, contexts are prepared through either retrieval-based or generation-based methods. The former involves retrieving relevant documents from a corpus like Wikipedia, whereas the latter uses generative models such as Large Language Models (LLMs) to generate the context. In this paper, we introduce a novel context preparation approach called HINTQA, which employs Automatic Hint Generation (HG) techniques. Unlike traditional methods, HINTQA prompts LLMs to produce hints about potential answers for the question rather than generating relevant context. We evaluate our approach across three QA datasets including TriviaQA, NaturalQuestions, and Web Questions, examining how the number and order of hints impact performance. Our findings show that the HINTQA surpasses both retrieval-based and generation-based approaches. We demonstrate that hints enhance the accuracy of answers more than retrieved and generated contexts.
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