FIRST: Faster Improved Listwise Reranking with Single Token Decoding (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing listwise LLMs lack efficiency as they provide ranking output in the form of a generated ordered sequence of candidate passage identifiers. |
| Approach: | They propose a listwise LLM reranking approach that leverages the first generated identifier to obtain a ranked ordering of the candidates. |
| Outcome: | The proposed approach accelerates inference by 50% while maintaining robust ranking performance with gains across BEIR benchmark. |
Similar Papers
GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs (2026.findings-acl)
Copied to clipboard
Meixiu Long, Duolin Sun, Dan Yang, Yihan Jiao, Lei Liu, Jiahai Wang, Binbin Hu, Yue Shen, Jie Feng, Zhehao Tan, Junjie Wang, Lianzhen Zhong, Jian Wang, Peng Wei, Jinjie Gu
| Challenge: | Existing rankers excel in lexical-matching scenarios, while they struggle with complex queries requiring deep reasoning. |
| Approach: | They propose a new paradigm that balances flexibility and context awareness to unlock the full potential of groupwise reranking. |
| Outcome: | The proposed approach achieves a state-of-the-art 65.2 NDCG@10 on BRIGHT and surpasses baselines by 2.1 points on R2MED while delivering a 6.4 inference speedup. |
Make Large Language Model a Better Ranker (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) demonstrate robust capabilities across various fields . current list-wise approaches fail in ranking tasks due to misalignment between ranking objectives and next-token prediction . |
| Approach: | They propose a large language model framework with Aligned Listwise Ranking Objectives (ALRO) this framework provides explicit feedback in a listwise manner by introducing soft lambda loss . |
| Outcome: | The proposed model outperforms existing recommendation methods and embedding-based recommendations without additional computational burdens. |
ReasonRank: Empowering Passage Ranking with Strong Reasoning Ability (2026.acl-long)
Copied to clipboard
| Challenge: | Existing rerankers perform poorly in complex ranking scenarios due to the scarcity of reasoning-intensive training data. |
| Approach: | They propose an automated reasoning-intensive training framework which generates high-quality training labels from training queries and passages. |
| Outcome: | The proposed model outperforms baselines significantly and achieves much lower latency than the pointwise reranker. |
ProRank: Prompt Warmup via Reinforcement Learning for Small Language Models Reranking (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent Large Language Models (LLMs) have demonstrated remarkable performance in document reranking tasks. |
| Approach: | They propose a two-stage training approach for document reranking using reinforcement learning and fine-grained score learning. |
| Outcome: | The proposed approach outperforms open-source and proprietary reranking models on BEIR benchmark. |
CoRanking: Collaborative Ranking with Small and Large Ranking Agents (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Listwise ranking based on Large Language Models (LLMs) has achieved state-of-the-art performance in Information Retrieval (IR) however, their effectiveness often depends on LLMs with massive parameter scales and computationally expensive sliding window processing, leading to substantial efficiency bottlenecks. |
| Approach: | They propose a Collaborative Ranking framework (CoRanking) for LLM-based listwise ranking based on large language models with massive parameter scales and computationally expensive sliding window processing. |
| Outcome: | The proposed framework reduces ranking latency by approximately 70% while improving effectiveness compared to the standalone large reranker. |
How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | a systematic and comprehensive empirical evaluation of state-of-the-art reranking methods is presented. |
| Approach: | They evaluate 22 reranking methods including 40 variants across established benchmarks . primary goal is to determine whether performance disparity exists between LLM-based reranters and lightweight counterparts based on novel queries . |
| Outcome: | The proposed methods perform better on familiar queries than lightweight models, the authors show . |
Lightweight reranking for language model generations (2024.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) can exhibit considerable variation in quality of sampled outputs. |
| Approach: | They propose a method for reranking LLM generations using pairwise statistics . they show strong improvements for selecting the best k generations for code generation tasks . |
| Outcome: | The proposed approach improves selection and generation quality for code generation tasks and autoformalization, summarization, and translation tasks. |
REALM: Recursive Relevance Modeling for LLM-based Document Re-Ranking (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing LLMs face ranking uncertainty, unstable top-k recovery, and high token cost due to token-intensive prompting. |
| Approach: | They propose a re-ranking framework that captures uncertainty and refines LLM-derived relevance through recursive Bayesian updates. |
| Outcome: | The proposed framework outperforms state-of-the-art re-rankers while reducing token usage and latency. |
UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of Rerankers (2023.emnlp-main)
Copied to clipboard
Jon Saad-Falcon, Omar Khattab, Keshav Santhanam, Radu Florian, Martin Franz, Salim Roukos, Avirup Sil, Md Sultan, Christopher Potts
| Challenge: | Existing methods for information retrieval tasks require large labeled datasets for fine-tuning, but they can experience significant drops in accuracy due to distribution shifts from the training to the target domain. |
| Approach: | They propose a method for using large language models to generate large numbers of synthetic queries cheaply using an expensive LLM. |
| Outcome: | The proposed method boosts zero-shot accuracy in long-tail domains and achieves substantially lower latency than standard reranking methods. |
Best Practices for Distilling Large Language Models into BERT for Web Search Ranking (2025.coling-industry)
Copied to clipboard
| Challenge: | Recent studies have highlighted the potential of Large Language Models (LLMs) as zero-shot relevance rankers. |
| Approach: | They propose to use a ranking loss to transfer ranking knowledge from LLMs to smaller models like BERT. |
| Outcome: | The proposed model has been successfully integrated into a commercial web search engine as of February 2024. |