# Guiding Retrieval using LLM-based Listwise Rankers

arXiv: https://arxiv.org/abs/2501.09186 · HTML: https://mlsanity.com/paper/2501.09186/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 38 references that are in the map.
- First posted on arXiv 15 Jan 2025.

## Builds on (most cited first, 23 of 23)

- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://mlsanity.com/paper/2004.12832/) (2020)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [Extending Context Window of Large Language Models via Positional Interpolation](https://mlsanity.com/paper/2306.15595/) (2023)
- [Document Ranking with a Pretrained Sequence-to-Sequence Model](https://mlsanity.com/paper/2003.06713/) (2020)
- [Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents](https://mlsanity.com/paper/2304.09542/) (2023)
- [Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting](https://mlsanity.com/paper/2306.17563/) (2023)
- [Fine-Tuning LLaMA for Multi-Stage Text Retrieval](https://mlsanity.com/paper/2310.08319/) (2023)
- [RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!](https://mlsanity.com/paper/2312.02724/) (2023)
- [Zero-Shot Listwise Document Reranking with a Large Language Model](https://mlsanity.com/paper/2305.02156/) (2023)
- [RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models](https://mlsanity.com/paper/2309.15088/) (2023)
- [FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions](https://mlsanity.com/paper/2403.15246/) (2024)
- [Scaling Down, LiTting Up: Efficient Zero-Shot Listwise Reranking with Seq2seq Encoder-Decoder Models](https://mlsanity.com/paper/2312.16098/) (2023)
- [Adaptive Re-Ranking with a Corpus Graph](https://mlsanity.com/paper/2208.08942/) (2022)
- [Generative Relevance Feedback with Large Language Models](https://mlsanity.com/paper/2304.13157/) (2023)
- [A Unified Framework for Learned Sparse Retrieval](https://mlsanity.com/paper/2303.13416/) (2023)
- [Top-Down Partitioning for Efficient List-Wise Ranking](https://mlsanity.com/paper/2405.14589/) (2024)
- [Generative and Pseudo-Relevant Feedback for Sparse, Dense and Learned Sparse Retrieval](https://mlsanity.com/paper/2305.07477/) (2023)
- [A Reproducibility Study of PLAID](https://mlsanity.com/paper/2404.14989/) (2024)
- [An Investigation of Prompt Variations for Zero-shot LLM-based Rankers](https://mlsanity.com/paper/2406.14117/) (2024)
- [rerankers: A Lightweight Python Library to Unify Ranking Methods](https://mlsanity.com/paper/2408.17344/) (2024)
- [Lexically-Accelerated Dense Retrieval](https://mlsanity.com/paper/2307.16779/) (2023)
- [Quam: Adaptive Retrieval through Query Affinity Modelling](https://mlsanity.com/paper/2410.20286/) (2024)

## Built on by (most cited first, 16 of 16)

- [EnrichIndex: Using LLMs to Enrich Retrieval Indices Offline](https://mlsanity.com/paper/2504.03598/) (2025)
- [Sample Efficient Demonstration Selection for In-Context Learning](https://mlsanity.com/paper/2506.08607/) (2025)
- [ChineseEcomQA: A Scalable E-commerce Concept Evaluation Benchmark for Large Language Models](https://mlsanity.com/paper/2502.20196/) (2025)
- [Beyond Sequential Reranking: Reranker-Guided Search Improves Reasoning Intensive Retrieval](https://mlsanity.com/paper/2509.07163/) (2025)
- [Evaluating Large Language Models for Cross-Lingual Retrieval](https://mlsanity.com/paper/2509.14749/) (2025)
- [LLM-guided Hierarchical Search for End-to-end Reasoning Intensive Retrieval](https://mlsanity.com/paper/2510.13217/) (2025)
- [Test-time Corpus Feedback: From Retrieval to RAG](https://mlsanity.com/paper/2508.15437/) (2025)
- [The Evolution of Reranking Models in Information Retrieval: From Heuristic Methods to Large Language Models](https://mlsanity.com/paper/2512.16236/) (2025)
- [Very Efficient Listwise Multimodal Reranking for Long Documents](https://mlsanity.com/paper/2605.11864/) (2026)
- [Token-Controlled Re-ranking for Sequential Recommendation via LLMs](https://mlsanity.com/paper/2511.17913/) (2025)
- [R3-SQL: Ranking Reward and Resampling for Text-to-SQL](https://mlsanity.com/paper/2604.25325/) (2026)
- [RLRF: Competitive Search Agent Design via Reinforcement Learning from Ranker Feedback](https://mlsanity.com/paper/2510.04096/) (2025)
- [Re-Rankers as Relevance Judges](https://mlsanity.com/paper/2601.04455/) (2026)
- [GraFine: Retrieval-Time Refinement for Efficient Graph RAG over Corpus Graphs](https://mlsanity.com/paper/2601.18579/) (2026)
- [UniRRM: Unified Reasoning Reward Models Across Languages and Evaluation Paradigms](https://mlsanity.com/paper/2609.05910/) (2026)
- [BoundaryMORPH: Budgeted Reranking via Active Set Selection for Diffuse Retrieval](https://mlsanity.com/paper/2609.27213/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have shown strong promise as rerankers, especially in ``listwise'' settings where an LLM is prompted to rerank several search results at once. However, this ``cascading'' retrieve-and-rerank approach is limited by the bounded recall problem: relevant documents not retrieved initially are permanently excluded from the final ranking. Adaptive retrieval techniques address this problem, but do not work with listwise rerankers because they assume a document's score is computed independently from other documents. In this paper, we propose an adaptation of an existing adaptive retrieval method that supports the listwise setting and helps guide the retrieval process itself (thereby overcoming the bounded recall problem for LLM rerankers). Specifically, our proposed algorithm merges results both from the initial ranking and feedback documents provided by the most relevant documents seen up to that point. Through extensive experiments across diverse LLM rerankers, first stage retrievers, and feedback sources, we demonstrate that our method can improve nDCG@10 by up to 13.23% and recall by 28.02%--all while keeping the total number of LLM inferences constant and overheads due to the adaptive process minimal. The work opens the door to leveraging LLM-based search in settings where the initial pool of results is limited, e.g., by legacy systems, or by the cost of deploying a semantic first-stage.