DemoRank: Selecting Effective Demonstrations for Large Language Models in Ranking Task
Abstract: Recently, there has been increasing interest in applying large language models (LLMs) as zero-shot passage rankers. However, few studies have explored how to select appropriate in-context demonstrations for the passage ranking task, which is the focus of this paper. Previous studies mainly apply a demonstration retriever to retrieve demonstrations and use top-$k$ demonstrations for in-context learning (ICL). Although effective, this approach overlooks the dependencies between demonstrations, leading to inferior performance of few-shot ICL in the passage ranking task. In this paper, we formulate the demonstration selection as a \textit{retrieve-then-rerank} process and introduce the DemoRank framework. In this framework, we first use LLM feedback to train a demonstration retriever and construct a novel dependency-aware training samples to train a demonstration reranker to improve few-shot ICL. The construction of such training samples not only considers demonstration dependencies but also performs in an efficient way. Extensive experiments demonstrate DemoRank's effectiveness in in-domain scenarios and strong generalization to out-of-domain scenarios.
Paper Type: Long
Research Area: Information Retrieval and Text Mining
Research Area Keywords: re-ranking
Contribution Types: NLP engineering experiment, Approaches to low-resource settings, Publicly available software and/or pre-trained models
Languages Studied: English
Submission Number: 1062
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