mlsanityData to 7 Oct 2026

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

arXiv 2402.15838 ↗First posted 24 Feb 2024cs.IRcited by 29

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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.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Feb 2024.

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Citing papers per year: 2024: 4; 2025: 15; 2026: 10.

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  1. 2024TourRank: Utilizing Large Language Models for Documents Ranking with a Tournament-Inspired Strategycited by 47
  2. 2025A Survey of Multimodal Retrieval-Augmented Generationcited by 37
  3. 2024JudgeRank: Leveraging Large Language Models for Reasoning-Intensive Rerankingcited by 34
  4. 2024Leveraging Passage Embeddings for Efficient Listwise Reranking with Large Language Modelscited by 20
  5. 2025The RAG Paradox: A Black-Box Attack Exploiting Unintentional Vulnerabilities in Retrieval-Augmented Generation Systemscited by 14
  6. 2025Shifting from Ranking to Set Selection for Retrieval Augmented Generationcited by 13
  7. 2025Who is in the Spotlight: The Hidden Bias Undermining Multimodal Retrieval-Augmented Generationcited by 9
  8. 2025Generating Plausible Distractors for Multiple-Choice Questions via Student Choice Predictioncited by 8
  9. 2025AcuRank: Uncertainty-Aware Adaptive Computation for Listwise Rerankingcited by 8
  10. 2024Self-Calibrated Listwise Reranking with Large Language Modelscited by 7
  11. 2025Loki's Dance of Illusions: A Comprehensive Survey of Hallucination in Large Language Modelscited by 6
  12. 2025Influence Guided Context Selection for Effective Retrieval-Augmented Generationcited by 6
17 more
  1. 2025RankArena: A Unified Platform for Evaluating Retrieval, Reranking and RAG with Human and LLM Feedbackcited by 5
  2. 2025SciRerankBench: Benchmarking Rerankers Towards Scientific Retrieval-Augmented Generated LLMscited by 5
  3. 2026Optimizing User Profiles via Contextual Bandits for Retrieval-Augmented LLM Personalizationcited by 5
  4. 2025How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Modelscited by 4
  5. 2026Learning from Emptiness: De-biasing Listwise Rerankers with Content-Agnostic Probability Calibrationcited by 4
  6. 2025E2Rank: Unifying Text Embedding and Listwise Reranking for Effective and Efficient Searchcited by 3
  7. 2025The Evolution of Reranking Models in Information Retrieval: From Heuristic Methods to Large Language Modelscited by 3
  8. 2026DeepEra: A Deep Evidence Reranking Agent for Scientific Retrieval-Augmented Generated Question Answeringcited by 2
  9. 2025R1-Ranker: Teaching LLM Rankers to Reasoncited by 1
  10. 2025You Only Forward Once: An Efficient Compositional Judging Paradigmcited by 1
  11. 2026MagicSelector: Joint Optimization for Agent Tool Selection via Counterfactual Decomposition and Progressive Rerankingcited by 1
  12. 2026Ranked by Position: Order Sensitivity as an Exploitable Attack Surface in LLM Listwise Recommenderscited by 1
  13. 2026BracketRank: Large Language Model Document Ranking via Reasoning-based Competitive Eliminationcited by 0
  14. 2026ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compressioncited by 0
  15. 2026Verbal-R3: Verbal Reranker as the Missing Bridge between Retrieval and Reasoningcited by 0
  16. 2026A Reranker for Orchestrating Heterogeneous Speech and Text Retrieverscited by 0
  17. 2026Local-to-Global Sentence-Level Graph Reranking for Scientific Synthesiscited by 0