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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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Wen, H., Shen, W., & Li, G. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3621835 · Cited by 1
FRASE: Frame-based Structured Representations for Generalizable SPARQL Query Generation
Diallo, P. A. K. K., & Zouaq, A. (2026) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/52g4z7jtim8o · Cited by 1
Dissecting GraphRAG: A Modular Analysis of Knowledge Structuring for Factoid Question Answering
Nishida, N., Munne, R. F., Liu, S., Tokunaga, N., Yamagata, Y., Cheng, F., Kozaki, K., & Matsumoto, Y. (2026) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl.a.615 · Cited by 1
Nakata, K., Miyashita, D., Ng, Y., Hoshi, Y., & Deguchi, J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91585-7_2 · Cited by 2
Generative Expression Constrained Knowledge-Based Decoding for Open Data
Lageweg, L., & Kruit, B. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-60626-7_17 · Cited by 1
Learning to Retrieve, Generate, and Compress: A Unified View of Efficient RAGPreprint
Brontes, F., Genesis, J., Noa, Z., & Nymphodoros, S. (2025) · Preprints.org · doi:10.20944/preprints202508.1211.v1 · Cited by 1
HSS: A Hierarchical Semantic Similarity Hard Negative Sampling Method for Dense Retrievers
Xie, X., Liu, F., Gai, S., Huang, Z., Hu, M., & Wang, A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-27818-1_25
KnowFIRES: A Knowledge-Graph Framework for Interpreting Retrieved Entities from Search
Arabzadeh, N., Golzadeh, K., Risi, C., Clarke, C. L. A., & Zhao, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-56069-9_15
ProSide: Knowledge Projector and Sideway for Pre-trained Language Models
He, C., Lu, G., & Shen, L. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-9434-8_5
Are Dense Retrieval Models Few-Shot Learners?
Cheng, H., Sun, S., Yang, S., Zheng, B., Yang, W., & Hu, H. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0119-6_39
Evaluating Entity Importance in a Cross-National Context using Crowdsourcing and Best–Worst Scaling
Perevalov, A., Abdollahi, S., Gottschalk, S., & Both, A. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.09.595
KGLM-QA: A Novel Approach for Knowledge Graph-Enhanced Large Language Models for Question Answering
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Few-Shot Multilingual Open-Domain QA from Five Examples
Jiang, F., Drummond, T. W., & Cohn, T. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00750
Negative Sampling Techniques in Dense Retrieval: A Survey
Wischounig, L., Abdallah, A., & Jatowt, A. (2026) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2026.findings-eacl.157 · Cited by 1
Towards Effective, Efficient and Explainable Neural Information Retrieval
Formal, T. (2023) · theses.fr (ABES) · doi:10.70675/0e6d7bcbz4ddbz45bczb909z78ab26702a56
Human-Machine Dialogue in the Medical Field. Using Dialog to Collect Important Patient Information
Liednikova, A. (2022) · theses.fr (ABES) · doi:10.70675/b572f8cbz5876z4683z828bzfb09085ddd78
Répondre aux questions visuelles à propos d'entités nommées
Lerner, P. J. (2023) · theses.fr (ABES) · doi:10.70675/78c96766zfc95z4da8zb67aze67555da6c75
Lei, Z., Xu, Y., & Chen, S. (2026) · Applied Sciences · doi:10.3390/app16083963
HaS: Accelerating RAG Through Homology-Aware Speculative Retrieval
Peng, P., Lin, W., Wu, W., Wang, X., & Liu, Y. (2026) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde65706.2026.00143
PretrainRL: Alleviating Factuality Hallucination of Large Language Models at the Beginning
Liu, L., LV, K., Chen, H., Zhang, W., Wang, Y., Liu, S., Tong, X. T., Yuan, Y., Wang, Y., Su, W., et al. (2026) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2026.findings-acl.910
Constructing Hard-Positive Query–Document Pairs for Dense Retrieval via Phrase Representativeness
Wu, Z., Zhang, R., & Nie, Z. (2026) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3805712.3809734
Vijayakumar, A. J., Rajendran, T., Ramkumar, S., & Rajagopal, M. (2026) · Research Square · doi:10.21203/rs.3.rs-10218278/v1
Surface Matching in Skill Recommenders: Incomplete Evaluation Hides Simplicity Bias
Veys, W., Lange, M. D., Decorte, J.-J., Develder, C., & Demeester, T. (2026) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3773078.3831842
An Evidence-Grounded Multimodal AI Framework for Historical Digital Personae in Cultural Heritage
Lipianina-Honcharenko, K., & Yurkiv, K. (2026) · Big Data and Cognitive Computing · doi:10.3390/bdcc10100351
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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