# Scaling Down, LiTting Up: Efficient Zero-Shot Listwise Reranking with Seq2seq Encoder-Decoder Models

arXiv: https://arxiv.org/abs/2312.16098 · HTML: https://mlsanity.com/paper/2312.16098/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2023.

## Built on by (most cited first, 26 of 26)

- [Ragnar"ok: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Track](https://mlsanity.com/paper/2406.16828/) (2024)
- [Rank-K: Test-Time Reasoning for Listwise Reranking](https://mlsanity.com/paper/2505.14432/) (2025)
- [RichRAG: Crafting Rich Responses for Multi-faceted Queries in Retrieval-Augmented Generation](https://mlsanity.com/paper/2406.12566/) (2024)
- [Guiding Retrieval using LLM-based Listwise Rankers](https://mlsanity.com/paper/2501.09186/) (2025)
- [Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation](https://mlsanity.com/paper/2502.02464/) (2025)
- [DeAR: Dual-Stage Document Reranking with Reasoning Agents via LLM Distillation](https://mlsanity.com/paper/2508.16998/) (2025)
- [rerankers: A Lightweight Python Library to Unify Ranking Methods](https://mlsanity.com/paper/2408.17344/) (2024)
- [Scalable In-context Ranking with Generative Models](https://mlsanity.com/paper/2510.05396/) (2025)
- [ReSLLM: Large Language Models are Strong Resource Selectors for Federated Search](https://mlsanity.com/paper/2401.17645/) (2024)
- [Self-Calibrated Listwise Reranking with Large Language Models](https://mlsanity.com/paper/2411.04602/) (2024)
- [RankArena: A Unified Platform for Evaluating Retrieval, Reranking and RAG with Human and LLM Feedback](https://mlsanity.com/paper/2508.05512/) (2025)
- [Optimizing User Profiles via Contextual Bandits for Retrieval-Augmented LLM Personalization](https://mlsanity.com/paper/2601.12078/) (2026)
- [An Early FIRST Reproduction and Improvements to Single-Token Decoding for Fast Listwise Reranking](https://mlsanity.com/paper/2411.05508/) (2024)
- [How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models](https://mlsanity.com/paper/2508.16757/) (2025)
- [When Claims Evolve: Evaluating and Enhancing the Robustness of Embedding Models Against Misinformation Edits](https://mlsanity.com/paper/2503.03417/) (2025)
- [Efficient and Responsible Adaptation of Large Language Models for Robust and Equitable Top-k Recommendations](https://mlsanity.com/paper/2501.04762/) (2025)
- [ConvKGYarn: Spinning Configurable and Scalable Conversational Knowledge Graph QA datasets with Large Language Models](https://mlsanity.com/paper/2408.05948/) (2024)
- [JointRank: Rank Large Set with Single Pass](https://mlsanity.com/paper/2506.22262/) (2025)
- [Offline Reasoning for Efficient Recommendation: LLM-Empowered Persona-Profiled Item Indexing](https://mlsanity.com/paper/2602.21756/) (2026)
- [F-GRPO: Factorized Group-Relative Policy Optimization for Unified Candidate Generation and Ranking](https://mlsanity.com/paper/2605.12995/) (2026)
- [RaCT: Ranking-aware Chain-of-Thought Optimization for LLMs](https://mlsanity.com/paper/2412.14405/) (2024)
- [Embedding-Based Context-Aware Reranker](https://mlsanity.com/paper/2510.13329/) (2025)
- [Inferential Question Answering](https://mlsanity.com/paper/2602.01239/) (2026)
- [Rethinking the Necessity of Adaptive Retrieval-Augmented Generation through the Lens of Adaptive Listwise Ranking](https://mlsanity.com/paper/2604.15621/) (2026)
- [DenoiseRank: Learning to Rank by Diffusion Models](https://mlsanity.com/paper/2604.20852/) (2026)
- [One Size Does Not Fit All! Dynamic Retriever and Generator Selection for RAG](https://mlsanity.com/paper/2609.17709/) (2026)

## Abstract (arXiv)

Recent work in zero-shot listwise reranking using LLMs has achieved state-of-the-art results. However, these methods are not without drawbacks. The proposed methods rely on large LLMs with billions of parameters and limited context sizes. This paper introduces LiT5-Distill and LiT5-Score, two methods for efficient zero-shot listwise reranking, leveraging T5 sequence-to-sequence encoder-decoder models. Our approaches demonstrate competitive reranking effectiveness compared to recent state-of-the-art LLM rerankers with substantially smaller models. Through LiT5-Score, we also explore the use of cross-attention to calculate relevance scores to perform reranking, eliminating the reliance on external passage relevance labels for training. We present a range of models from 220M parameters to 3B parameters, all with strong reranking results, challenging the necessity of large-scale models for effective zero-shot reranking and opening avenues for more efficient listwise reranking solutions. We provide code and scripts to reproduce our results at https://github.com/castorini/LiT5.