# ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrieval

arXiv: https://arxiv.org/abs/2402.15838 · HTML: https://mlsanity.com/paper/2402.15838/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2024.

## Built on by (most cited first, 30 of 30)

- [TourRank: Utilizing Large Language Models for Documents Ranking with a Tournament-Inspired Strategy](https://mlsanity.com/paper/2406.11678/) (2024)
- [A Survey of Multimodal Retrieval-Augmented Generation](https://mlsanity.com/paper/2504.08748/) (2025)
- [JudgeRank: Leveraging Large Language Models for Reasoning-Intensive Reranking](https://mlsanity.com/paper/2411.00142/) (2024)
- [Leveraging Passage Embeddings for Efficient Listwise Reranking with Large Language Models](https://mlsanity.com/paper/2406.14848/) (2024)
- [Shifting from Ranking to Set Selection for Retrieval Augmented Generation](https://mlsanity.com/paper/2507.06838/) (2025)
- [The RAG Paradox: A Black-Box Attack Exploiting Unintentional Vulnerabilities in Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2502.20995/) (2025)
- [Who is in the Spotlight: The Hidden Bias Undermining Multimodal Retrieval-Augmented Generation](https://mlsanity.com/paper/2506.11063/) (2025)
- [Generating Plausible Distractors for Multiple-Choice Questions via Student Choice Prediction](https://mlsanity.com/paper/2501.13125/) (2025)
- [AcuRank: Uncertainty-Aware Adaptive Computation for Listwise Reranking](https://mlsanity.com/paper/2505.18512/) (2025)
- [Self-Calibrated Listwise Reranking with Large Language Models](https://mlsanity.com/paper/2411.04602/) (2024)
- [Loki's Dance of Illusions: A Comprehensive Survey of Hallucination in Large Language Models](https://mlsanity.com/paper/2507.02870/) (2025)
- [Influence Guided Context Selection for Effective Retrieval-Augmented Generation](https://mlsanity.com/paper/2509.21359/) (2025)
- [RankArena: A Unified Platform for Evaluating Retrieval, Reranking and RAG with Human and LLM Feedback](https://mlsanity.com/paper/2508.05512/) (2025)
- [SciRerankBench: Benchmarking Rerankers Towards Scientific Retrieval-Augmented Generated LLMs](https://mlsanity.com/paper/2508.08742/) (2025)
- [Optimizing User Profiles via Contextual Bandits for Retrieval-Augmented LLM Personalization](https://mlsanity.com/paper/2601.12078/) (2026)
- [How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models](https://mlsanity.com/paper/2508.16757/) (2025)
- [E2Rank: Unifying Text Embedding and Listwise Reranking for Effective and Efficient Search](https://mlsanity.com/paper/2510.22733/) (2025)
- [Learning from Emptiness: De-biasing Listwise Rerankers with Content-Agnostic Probability Calibration](https://mlsanity.com/paper/2604.10150/) (2026)
- [The Evolution of Reranking Models in Information Retrieval: From Heuristic Methods to Large Language Models](https://mlsanity.com/paper/2512.16236/) (2025)
- [DeepEra: A Deep Evidence Reranking Agent for Scientific Retrieval-Augmented Generated Question Answering](https://mlsanity.com/paper/2601.16478/) (2026)
- [R1-Ranker: Teaching LLM Rankers to Reason](https://mlsanity.com/paper/2506.21638/) (2025)
- [You Only Forward Once: An Efficient Compositional Judging Paradigm](https://mlsanity.com/paper/2511.16600/) (2025)
- [MagicSelector: Joint Optimization for Agent Tool Selection via Counterfactual Decomposition and Progressive Reranking](https://mlsanity.com/paper/2607.17751/) (2026)
- [Ranked by Position: Order Sensitivity as an Exploitable Attack Surface in LLM Listwise Recommenders](https://mlsanity.com/paper/2607.24869/) (2026)
- [BracketRank: Large Language Model Document Ranking via Reasoning-based Competitive Elimination](https://mlsanity.com/paper/2604.08834/) (2026)
- [ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compression](https://mlsanity.com/paper/2604.22180/) (2026)
- [Verbal-R3: Verbal Reranker as the Missing Bridge between Retrieval and Reasoning](https://mlsanity.com/paper/2605.01399/) (2026)
- [A Reranker for Orchestrating Heterogeneous Speech and Text Retrievers](https://mlsanity.com/paper/2608.26194/) (2026)
- [Local-to-Global Sentence-Level Graph Reranking for Scientific Synthesis](https://mlsanity.com/paper/2608.30525/) (2026)
- [Recall What Is Missing: Composing Evidence from Long-Term Agent Memory](https://mlsanity.com/paper/2610.15642/) (2026)

## Abstract (arXiv)

We propose ListT5, a novel reranking approach based on Fusion-in-Decoder (FiD) that handles multiple candidate passages at both train and inference time. We also introduce an efficient inference framework for listwise ranking based on m-ary tournament sort with output caching. We evaluate and compare our model on the BEIR benchmark for zero-shot retrieval task, demonstrating that ListT5 (1) outperforms the state-of-the-art RankT5 baseline with a notable +1.3 gain in the average NDCG@10 score, (2) has an efficiency comparable to pointwise ranking models and surpasses the efficiency of previous listwise ranking models, and (3) overcomes the lost-in-the-middle problem of previous listwise rerankers. Our code, model checkpoints, and the evaluation framework are fully open-sourced at urlhttps://github.com/soyoung97/ListT5.