mlsanityData to 7 Oct 2026

LLM-VPRF: Large Language Model Based Vector Pseudo Relevance Feedback

arXiv 2504.01448 ↗First posted 2 Apr 2025cs.IR cs.LGcited by 2

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Abstract (arXiv)

Vector Pseudo Relevance Feedback (VPRF) has shown promising results in improving BERT-based dense retrieval systems through iterative refinement of query representations. This paper investigates the generalizability of VPRF to Large Language Model (LLM) based dense retrievers. We introduce LLM-VPRF and evaluate its effectiveness across multiple benchmark datasets, analyzing how different LLMs impact the feedback mechanism. Our results demonstrate that VPRF's benefits successfully extend to LLM architectures, establishing it as a robust technique for enhancing dense retrieval performance regardless of the underlying models. This work bridges the gap between VPRF with traditional BERT-based dense retrievers and modern LLMs, while providing insights into their future directions.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 26 of its 46 references that are in the map. First posted on arXiv 2 Apr 2025.

Builds on

26 of 46 references.

References · 26

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14 more
  1. 2023Query Expansion by Prompting Large Language Modelscited by 56
  2. 2020Distilling Dense Representations for Ranking using Tightly-Coupled Teacherscited by 52
  3. 2020RepBERT: Contextualized Text Embeddings for First-Stage Retrievalcited by 46
  4. 2021Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedbackcited by 45
  5. 2023Open-source Large Language Models are Strong Zero-shot Query Likelihood Models for Document Rankingcited by 44
  6. 2021Pseudo-Relevance Feedback for Multiple Representation Dense Retrievalcited by 31
  7. 2024PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrievalcited by 30
  8. 2021Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and Pitfallscited by 26
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  11. 2021Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback: A Reproducibility Studycited by 13
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  14. 2021PGT: Pseudo Relevance Feedback Using a Graph-Based Transformercited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Test-Time Compute for Frozen Embedding Models through Agentic Program Searchcited by 0
  2. 2026PLAID-PRF: Pseudo-Relevance Feedback with Centroid-like Tokens in PLAIDcited by 0