A Systematic Study of Pseudo-Relevance Feedback with LLMs
Pseudo-relevance feedback (PRF) methods built on large language models (LLMs) can be organized along two key design dimensions: the feedback source, which is where the feedback text is derived from and the feedback model, which is how the given feedback text is used to refine the query representation. However, the independent role that each dimension plays is unclear, as both are often entangled in empirical evaluations. In this paper, we address this gap by systematically studying how the choice of feedback source and feedback model impact PRF effectiveness through controlled experimentation. Across 13 low-resource BEIR tasks with five LLM PRF methods, our results show: (1) the choice of...
| Authors | Nour Jedidi, Jimmy Lin |
|---|---|
| Primary category | cs.IR |
| Published | 11 March 2026 |
| Last updated at source | 11 March 2026 |
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