| Challenge: | Existing methods for text ranking have improved performance, but there are still challenges. |
| Approach: | They propose a method that learns to re-rank the text retrieved for a given query by learning to predict the most relevant passage based on a latent preference matrix. |
| Outcome: | The proposed method outperforms all prior methods on datasets with extensive results. |
Similar Papers
Embedding Meta-Textual Information for Improved Learning to Rank (2020.coling-main)
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| Challenge: | a neural representation learning approach has not been extended to meta-textual information that is readily available for many IR tasks. |
| Approach: | They propose a framework that learns embeddings for meta-textual categories and optimizes a pairwise ranking objective for improved matching based on combined embedds of textual and meta-tactile information. |
| Outcome: | The proposed framework improves cross-lingual retrieval in the Wikipedia domain and Patent domain. |
ProRank: Prompt Warmup via Reinforcement Learning for Small Language Models Reranking (2026.findings-acl)
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| 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. |
EcoRank: Budget-Constrained Text Re-ranking Using Large Language Models (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated dominant performance in text re-ranking. |
| Approach: | They propose a suite of budget-constrained methods to perform text re-ranking using LLMs. |
| Outcome: | The proposed method outperforms other budget-aware methods on four datasets. |
GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs (2026.findings-acl)
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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. |
A Study of Latent Structured Prediction Approaches to Passage Reranking (N19-1)
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| Challenge: | a structured output framework is useful for learning to rank problems . current approaches for answer sentence reranking are mostly based on pairwise ranking signals or simple binary classification. |
| Approach: | They propose a structured output approach which regards rankings as latent variables . they propose an inference procedure to find the max-violating ranking based on decomposition of the corresponding loss. |
| Outcome: | The proposed approach solves the optimization problem on WikiQA and TREC13 datasets. |
Pre-Training Methods for Question Reranking (2024.eacl-short)
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| Challenge: | Existing methods for Question Answering to search for semantically similar questions are not suitable for new questions. |
| Approach: | They propose an unsupervised method for retrieving and ranking questions . they use a question retrieval model and a selection model to rerank questions based on their relevance . |
| Outcome: | The proposed method achieves state-of-the-art performance on QRC and Quora-match datasets . it provides better and cheaper access to answers than the system generated them . |
SDR: Efficient Neural Re-ranking using Succinct Document Representation (2022.acl-long)
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| Challenge: | BERT based ranking models have been successful on various information retrieval tasks, but they are prone to storage and network fetching latency. |
| Approach: | They propose a late-interaction architecture that allows pre-computation of intermediate document representations, thus reducing latency. |
| Outcome: | The proposed model achieves 4x–11.6x higher compression rates on the MSMARCO passage re-reranking task compared to existing methods. |
Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning (2026.acl-long)
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| Challenge: | Current reranking models are optimized on static human annotations in isolation, decoupled from the downstream generation process. |
| Approach: | They propose a reinforcement learning framework that directly aligns reranking with LLM's generation quality. |
| Outcome: | Experiments on knowledge-intensive benchmarks show that RRPO outperforms strong baselines. |
FIRST: Faster Improved Listwise Reranking with Single Token Decoding (2024.emnlp-main)
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| 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. |
Pretrained Transformers for Text Ranking: BERT and Beyond (2021.naacl-tutorials)
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| Challenge: | This tutorial provides an overview of text ranking using neural network architectures known as transformers. |
| Approach: | This tutorial provides an overview of text ranking with neural network architectures known as transformers. |
| Outcome: | This tutorial provides an overview of text ranking with neural network architectures known as transformers. |