Papers with ranking ability
Consolidating Ranking and Relevance Predictions of Large Language Models through Post-Processing (2024.emnlp-main)
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| Challenge: | Existing approaches to generate relevance labels for large language models have not been successful in generating relevance labels. |
| Approach: | They propose a method to combine LLM relevance labels with ranking abilities . they take both LLM generated relevance labels and pairwise preferences . |
| Outcome: | The proposed method balances the ranking and labeling abilities of large language models . it takes both LLM generated relevance labels and pairwise preferences . |
RRADistill: Distilling LLMs’ Passage Ranking Ability for Long-Tail Queries Document Re-Ranking on a Search Engine (2024.emnlp-industry)
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Nayoung Choi, Youngjune Lee, Gyu-Hwung Cho, Haeyu Jeong, Jungmin Kong, Saehun Kim, Keunchan Park, Sarah Cho, Inchang Jeong, Gyohee Nam, Sunghoon Han, Wonil Yang, Jaeho Choi
| Challenge: | Large Language Models excel at understanding the semantic relationships between queries and documents, even with lengthy and complex long-tail queries. |
| Approach: | They propose an efficient label generation pipeline and novel sLLM training methods for both encoder and decoder models. |
| Outcome: | The proposed method improves re-ranking for long-tail queries on a Korean-based search platform. |
Have a Cake and Eat it Too: Assessing Discriminating Performance of an Intelligibility Index Obtained from a Reduced Sample Size (2020.lrec-1)
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| Challenge: | Random vs. phonetically motivated reduction of linguistic material in speech disordered populations has a higher ranking ability than when the sample size is reduced based on a phonetic criterium. |
| Approach: | They investigate random vs. phonetically motivated reduction of linguistic material in speech disordered populations and their impact on the discrimination classifier. |
| Outcome: | The proposed method is reliable and efficient, and it is of significant interest to clinicians and patients. |
VLM Is a Strong Reranker: Advancing Multimodal Retrieval-augmented Generation via Knowledge-enhanced Reranking and Noise-injected Training (2025.findings-emnlp)
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| Challenge: | a significant drawback of Vision-language Models is their reliance on static training data, leading to outdated information and limited contextual awareness. |
| Approach: | They propose a framework with knowledge-enhanced reranking and noise-injected training to improve the VLM's ranking ability. |
| Outcome: | The proposed framework is based on a simple yet effective instruction template and is able to induce its ranking ability and serve it as a reranker to precisely filter the top-k retrieved images. |
LPZero: Language Model Zero-cost Proxy Search from Zero (2024.findings-emnlp)
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| Challenge: | Existing zero-cost (ZC) proxies rely on expert knowledge and incur significant trial-and-error costs. |
| Approach: | They propose a framework that automatically designs zero-cost (ZC) proxies for various tasks and incorporates genetic programming to find the optimal symbolic composition. |
| Outcome: | The proposed framework achieves higher ranking consistency than human-designed proxies on NLP tasks. |
ReasonRank: Empowering Passage Ranking with Strong Reasoning Ability (2026.acl-long)
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| 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. |