Papers with RankGPT
BracketRank: Large Language Model Document Ranking via Reasoning-based Competitive Elimination (2026.acl-long)
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| Challenge: | Existing lists of document ranking methods lack robust performance across domains. |
| Approach: | They propose a reasoning-driven competitive elimination framework that optimises group sizes based on LLM context limits and reasoning-enhanced prompts. |
| Outcome: | The proposed method outperforms RankGPT and other state-of-the-art methods on datasets with a 77.90 NDCG@5 score and 54.66 average NDGC@10 on BEIR datasets. |
DeAR: Dual-Stage Document Reranking with Reasoning Agents via LLM Distillation (2025.findings-emnlp)
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| Challenge: | DeAR is an open-source framework that decouples the tasks of LLMs with holistic cross-document analysis. |
| Approach: | They propose an open-source framework that decouples relevance scoring with holistic cross-document analysis. |
| Outcome: | The proposed framework outperforms open-source frameworks in QA and open-domain QA. |
Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankers (2024.naacl-long)
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| Challenge: | Recent studies have shown that Large Language Models (LLMs) are more efficient in natural language understanding tasks. |
| Approach: | They evaluate large language models (LLMs) using a TREC Fair Ranking dataset . they assess fairness from both user and content perspectives . |
| Outcome: | The proposed model outperforms the existing models in the fair ranking task. |
Query-Focused Retrieval Heads Improve Long-Context Reasoning and Re-ranking (2025.emnlp-main)
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| Challenge: | Recent work has identified retrieval heads as a subset of attention heads responsible for retrieving salient information in long-context language models. |
| Approach: | They introduce a retrieval head that uses attention scores to enhance retrieval from long context . they use QRRetriever to select the most relevant parts with the highest retrieval scores . |
| Outcome: | The proposed retrieval heads outperform other retrieval-based retrieval retrievers on BEIR benchmarks. |