The DOI matches its Crossref record; 30 of the 33 references it cites were found in a registry or an index.
Simple Entity-Centric Questions Challenge Dense Retrievers
Open accessAbstract
Open-domain question answering has exploded in popularity recently due to the success of dense retrieval models, which have surpassed sparse models using only a few supervised training examples.However, in this paper, we demonstrate current dense models are not yet the holy grail of retrieval.We first construct EntityQuestions, a set of simple, entityrich questions based on facts from Wikidata (e.g., "Where was Arve Furset born?"), and observe that dense retrievers drastically underperform sparse methods.We investigate this issue and uncover that dense retrievers can only generalize to common entities unless the question pattern is explicitly observed during training.We discuss two simple solutions towards addressing this critical problem.First, we demonstrate that data augmentation is unable to fix the generalization problem.Second, we argue a more robust passage encoder helps facilitate better question adaptation using specialized question encoders.We hope our work can shed light on the challenges in creating a robust, universal dense retriever that works well across different input distributions. 1
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Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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Dense passage retrieval for open-domain question answering
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TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
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Reading Wikipedia to answer open-domain questions
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Self-citationLatent retrieval for weakly supervised open domain question answering
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Cited by
Sorted by citations
Retrieval-Augmented Generation for Large Language Models: A Survey
Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., Wang, M., & Wang, H. (2023) · arXiv · doi:10.48550/arxiv.2312.10997 · Cited by 876
“In a further stage of generator training, the model acknowledges the critical role of entity semantics in the representation learning of textual data for retrieval, as highlighted by [ Sciavolino et al. , 2021, Zhang et al. , 2019 ] .”
MethodMallen, A., Asai, A., Zhong, V., Das, R., Khashabi, D., & Hajishirzi, H. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.546 · Cited by 340
Information Retrieval: Recent Advances and Beyond
Hambarde, K., & Proença, H. P. M. C. (2023) · IEEE Access · doi:10.1109/access.2023.3295776 · Cited by 212
Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge
Soudani, H., Kanoulas, E., & Hasibi, F. (2024) · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (SIGIR-AP) · doi:10.1145/3673791.3698415 · Cited by 136
Improving Passage Retrieval with Zero-Shot Question Generation
Sachan, D., Lewis, M., Joshi, M., Aghajanyan, A., Yih, W.-T., Pineau, J., & Zettlemoyer, L. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.249 · Cited by 90
Metadata
- Authors
- Sciavolino, C., Zhong, Z., Lee, J., & Chen, D.
- Affiliations
- Princeton University (US)
- Venue
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Published
- 2021
- Pages
- 6138-6148
- OpenAlex
- W3201233724
- Cited by
- 98
- References
- 33
- Citations by year
- 2026: 12 · 2025: 15 · 2024: 16 · 2023: 33 · 2022: 20 · 2021: 2
- FWCI
- 7.92
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, public-domain)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- dense retrieval, open-domain question answering, Wikidata, data augmentation
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