InstUPR : Instruction-based Unsupervised Passage Reranking with Large Language Models
This paper introduces InstUPR, an unsupervised passage reranking method based on large language models (LLMs). Different from existing approaches that rely on extensive training with query-document pairs or retrieval-specific instructions, our method leverages the instruction-following capabilities of instruction-tuned LLMs for passage reranking without any additional fine-tuning. To achieve this, we introduce a soft score aggregation technique and employ pairwise reranking for unsupervised passage reranking. Experiments on the BEIR benchmark demonstrate that InstUPR outperforms unsupervised baselines as well as an instruction-tuned reranker, highlighting its effectiveness and superiority. Source code to reproduce all experiments is open-sourced at https://github.com/MiuLab/InstUPR
Code (1)
Tasks
Instruction FollowingPassage RerankingRerankingRetrievalSimilar Papers 제목 키워드 기반
KaLM-Reranker-V1: Fast but Not Late Interaction for Compressed Document Reranking
As retrieval systems scale, high-quality reranking becomes increasingly important. However, most existing rerankers, whether encoder-based or decoder-based, jointly encode the query and passage, tightly coupling their co…
Hybrid and Collaborative Passage Reranking
In passage retrieval system, the initial passage retrieval results may be unsatisfactory, which can be refined by a reranking scheme. Existing solutions to passage reranking focus on enriching the interaction between que…
Passage RerankingPassage RetrievalRerankingRetrievalRankFlow: A Multi-Role Collaborative Reranking Workflow Utilizing Large Language Models
In an Information Retrieval (IR) system, reranking plays a critical role by sorting candidate passages according to their relevance to a specific query. This process demands a nuanced understanding of the variations amon…
Information RetrievalRerankingPassage-specific Prompt Tuning for Passage Reranking in Question Answering with Large Language Models
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 passag…
Open-Domain Question AnsweringPassage RerankingPassage RetrievalQuestion Answering+1Rider: Reader-Guided Passage Reranking for Open-Domain Question Answering
Current open-domain question answering systems often follow a Retriever-Reader architecture, where the retriever first retrieves relevant passages and the reader then reads the retrieved passages to form an answer. In th…
Natural QuestionsOpen-Domain Question AnsweringPassage RerankingQuestion Answering+3