Effective passage retrieval and reranking methods have been widely utilized to identify suitable candidates in open-domain question answering tasks, recent studies have resorted to LLMs for reranking the retrieved passages by the log-likelihood of the question conditioned on each passage. Although these methods have demonstrated promising results, the performance is notably sensitive to the human-written prompt (or hard prompt), and fine-tuning LLMs can be computationally intensive and time-consuming. Furthermore, this approach limits the leverage of question-passage relevance pairs and passage-specific knowledge to enhance the ranking capabilities of LLMs. In this paper, we propose passage-specific prompt tuning for reranking in open-domain question answering (PSPT): a parameter-efficient method that fine-tunes learnable passage-specific soft prompts, incorporating passage-specific knowledge from a limited set of question-passage relevance pairs. The method involves ranking retrieved passages based on the log-likelihood of the model generating the question conditioned on each passage and the learned soft prompt. We conducted extensive experiments utilizing the Llama-2-chat-7B model across three publicly available open-domain question answering datasets and the results demonstrate the effectiveness of the proposed approach.

翻译：有效的段落检索与重排序方法已被广泛应用于开放域问答任务中以识别合适的候选段落。近期研究倾向于利用大语言模型，通过计算给定每个段落条件下问题的对数似然来对检索到的段落进行重排序。尽管这些方法已展现出有希望的结果，但其性能对人类编写的提示（或硬提示）尤为敏感，且对大语言模型进行微调可能计算密集且耗时。此外，该方法限制了利用问题-段落相关性对和段落特定知识来增强大语言模型排序能力的机会。本文提出用于开放域问答重排序的段落特定提示调优方法：一种参数高效的方法，通过微调可学习的段落特定软提示，从有限的问题-段落相关性对中融入段落特定知识。该方法基于模型在给定每个段落及学习到的软提示条件下生成问题的对数似然对检索到的段落进行排序。我们利用Llama-2-chat-7B模型在三个公开可用的开放域问答数据集上进行了大量实验，结果证明了所提方法的有效性。