  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.
