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Search for Coverage: Learning Coverage-Aware Retrieval with Augmented Sub-Question Answerability
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Reciprocal rank fusion outperforms condorcet and individual rank learning methods
Cormack, G. V., Clarke, C. L. A., & Buettcher, S. (2009) · Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/1571941.1572114 · Cited by 797
Grattafiori, A., Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Vaughan, A., et al. (2024) · arXiv · doi:10.48550/arxiv.2407.21783 · Cited by 505 · cited 2×
“We mitigate this shortcoming by labeling relevant documents with different grades through sub-question answerability [13, 36] using Llama-3 70B model [29].”
Research works printed but not linked: 41
- Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, et al., 2016. MS MARCO: A human generated machine reading comprehension dataset. showeprint1611.09268
- 10.1145/290941.291025 · DOI · Paper page
- 10.18653/v1/2025.naacl-long.431 · DOI
- Jianlyu Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, and Zheng Liu. 2024. M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation. In Findings of ACL. 2318-2335.
- 10.1145/1390334.1390446 · DOI · Paper page
- Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, and Jimmy Lin. 2025. Overview of the TREC 2021 deep learning track. showeprint2507.08191
- Lawrie Dawn, Macavaney Sean, Mayfield James, Mcnamee Paul, W Oard Douglas, Soldaini Luca, and Yang Eugene. 2025. Overview of the TREC 2024 NeuCLIR track. showeprint2509.14355
- 10.1145/3626772.3657871 · DOI
- Alexander Fabbri, Irene Li, Tianwei She, Suyi Li, and Dragomir Radev. 2019. Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model. In Proc. of ACL. 1074-1084.
- Naghmeh Farzi and Laura Dietz. 2024b. Pencils down! Automatic rubric-based evaluation of retrieve/generate systems. In Proc. of SIGIR. 175-184.
- Tianyu Gao, Howard Yen, Jiatong Yu, and Danqi Chen. 2023. Enabling large language models to generate text with citations. In Proc. of EMNLP. 6465-6488.
- Max Grusky, Mor Naaman, and Yoav Artzi. 2018. Newsroom: A dataset of 1.3 million summaries with diverse extractive strategies. In Proc. of NAACL-HLT. 708-719.
- Matthew Henderson, Rami Al-Rfou, Brian Strope, Yun hsuan Sung, Laszlo Lukacs, Ruiqi Guo, Sanjiv Kumar, Balint Miklos, and Ray Kurzweil. 2017. Efficient Natural Language Response Suggestion for Smart Reply. showeprint1705.00652
- 10.1007/978-3-032-21300-6_12 · DOI
- Jia-Huei Ju, Suzan Verberne, Maarten de Rijke, and Andrew Yates. 2025. Controlled retrieval-augmented context evaluation for long-form RAG. In Findings of EMNLP. 21102-21121.
- Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020. Dense Passage Retrieval for Open-Domain Question Answering. In Proc. of EMNLP. 6769-6781.
- Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019. Natural Questions: A Benchmark for Question Answering Research. Trans. of the ACL, Vol. 7 (2019), 452-466.
- Dahyun Lee, Yongrae Jo, Haeju Park, and Moontae Lee. 2025. Shifting from ranking to set selection for retrieval augmented generation. In Proc. of ACL. 17606-17619.
- Kenton Lee, Ming-Wei Chang, and Kristina Toutanova. 2019. Latent retrieval for weakly supervised open domain question answering. In Proc. of ACL. 6086-6096.
- Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2020. Retrieval-augmented generation for knowledge-intensive NLP tasks. Proc. of NIPS.
- 10.18653/v1/2023.findings-emnlp.423 · DOI
- 10.1145/3726302.3730135 · DOI
- 10.1145/3626772.3657846 · DOI
- 10.18653/v1/2021.emnlp-main.560 · DOI
- Zach Nussbaum, John Xavier Morris, Andriy Mulyar, and Brandon Duderstadt. 2025. Nomic Embed: Training a Reproducible Long Context Text Embedder. TMLR.
- Arnold Overwijk Chenyan Xiong Xiao Liu Cameron VandenBerg and Jamie Callan. 2022. Clueweb22: 10 billion web documents with visual and semantic information. showeprint2211.15848 · DOI · Paper page
- Yingqi Qu, Yuchen Ding, Jing Liu, Kai Liu, Ruiyang Ren, Wayne Xin Zhao, Daxiang Dong, Hua Wu, and Haifeng Wang. 2021. RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering. In Proc. of NAACL-HLT. 5835-5847.
- 10.1145/3726302.3730275 · DOI
- Chris Samarinas, Alexander Krubner, Alireza Salemi, Youngwoo Kim, and Hamed Zamani. 2025. Beyond Factual Accuracy: Evaluating Coverage of Diverse Factual Information in Long-form Text Generation. In Findings of ACL. 13468-13482.
- David P Sander and Laura Dietz. 2021. EXAM: How to evaluate retrieve-and-generate systems for users who do not (yet) know what they want. DESIRES, 136-146.
- 10.18653/v1/2022.emnlp-main.566 · DOI · Paper page
- Haochen Tan, Zhijiang Guo, Zhan Shi, Lu Xu, Zhili Liu, Yunlong Feng, Xiaoguang Li, Yasheng Wang, Lifeng Shang, Qun Liu, and Linqi Song. 2024. ProxyQA: An alternative framework for evaluating long-form text generation with large language models. In Proc. of ACL. 6806-6827.
- Nandan Thakur, Nils Reimers, Andreas Rücklé, Abhishek Srivastava, and Iryna Gurevych. 2021. BEIR: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models. In Proc. of NeurIPS.
- Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2023. Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions. In Proc. of ACL. 10014-10037.
- 10.3115/1073483.1073520 · DOI
- Benjamin Warner, Antoine Chaffin, Benjamin Clavié, Orion Weller, Oskar Hallström, Said Taghadouini, Alexis Gallagher, Raja Biswas, Faisal Ladhak, Tom Aarsen, Griffin Thomas Adams, Jeremy Howard, and Iacopo Poli. 2025. Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference. In Proc. of ACL. 2526-2547.
- 10.1023/A:1026516825764 · DOI
- Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul N. Bennett, Junaid Ahmed, and Arnold Overwijk. 2021. Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval. In Proc. of ICLR.
- 10.52202/079017-0335 · DOI
- Wen-Tau Yih, Kristina Toutanova, John C Platt, and Christopher Meek. 2011. Learning Discriminative Projections for Text Similarity Measures. In Proc. of CoNLL. 247-256.
- 10.1145/3583780.3615050 · DOI
Metadata
- Authors
- Ju, J.-H., Yang, E., Adriaanse, T., Verberne, S., & Yates, A.
- Venue
- Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval
- Publisher
- ACM
- Published
- 2026-07-19
- Pages
- 723-733
- Cited by
- 0
- References
- 2
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- Ju, J.-H., Yang, E., Adriaanse, T., Verberne, S., & Yates, A. (2026). Search for Coverage: Learning Coverage-Aware Retrieval with Augmented Sub-Question Answerability. Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval. https://doi.org/10.1145/3805712.3809752
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.