UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation
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Daixuan Cheng, Shaohan Huang, Junyu Bi, Yuefeng Zhan, Jianfeng Liu, Yujing Wang, Hao Sun, Furu Wei, Weiwei Deng, Qi Zhang. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.
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UPRISE improves LLMs' zero-shot capabilities of long-form question-answering.
“UPRISE (Cheng et al., 2023) finetunes a lightweight prompt retriever to automatically retrieve prompts tailored to specific inputs, improving LLMs' zero-shot capabilities of long-form question-answering.”
From The AI Hippocampus: How Far are We From Human Memory? (2026) · cites this paper as Cheng et al., 2023
The Introduction and Method describe a fine-tuned retriever that selects prompts for zero-shot inputs. Section 5.1 reports improved performance on closed-book question answering and reading comprehension, but the paper does not establish an improvement specifically for long-form question answering. (LLM, conf=0.86)
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Li, X., Peng, S., Wakamiya, S., & Aramaki, E. (2024) · doi:10.2196/preprints.68320 · Cited by 1 · see citing works
A Comparative Analysis of Retrieval-Augmented Generation and Crowdsourcing for Fact-Checking
Bona, F. B. D., Barbera, D. L., Mizzaro, S., & Roitero, K. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88714-7_44
General Quality Evaluation Metric Towards Demonstrations for In-Context Learning
Wang, H., Wu, J., Sun, H., Xia, Z., Cheng, D., Wang, J., Qi, Q., & Liao, J. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3606230
Self-citationImproving Cloze Distractor Generation Through Retrieval-based Example Selection
Dong, J., Bai, J., Zhang, J., Li, C., Lin, X., & Rong, W. (2025) · IEEE Smart World Congress (SWC) · doi:10.1109/swc65939.2025.00193
Prompt Tuning and Retrieval in Open-Vocabulary Object Detection for Configurable Robot Vision
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Xia, Y., Kamsin, A., Pan, Z., & Zhang, S. (2026) · Journal of Intelligent Information Systems · doi:10.1007/s10844-026-01038-7
RAG-Driven Framework for Digraph-Based Automated Assembly Process Generation
Tian, S., Yu, D., Liu, Z., Cui, L., Li, Z., & Liu, H. (2025) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/iccc68654.2025.11437599
Attribution Techniques for Mitigating Hallucinated Information in RAG Systems: A Survey
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Empowering Hybrid RAG Models with Knowledge Graphs for Enhanced Precision and Reliability
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The role of diversity in few-shot learning for multi-label classification of legal documents
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CGR: A Budget-Aware Closed-Loop Framework for Efficient Knowledge Graph Question Answering
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- Authors
- Cheng, D., Huang, S., Bi, J., Zhan, Y., Liu, J., Wang, Y., Sun, H., Wei, F., Deng, W., & Zhang, Q.
- Affiliations
- Microsoft (United States) (US)
- Venue
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Published
- 2023
- Pages
- 12318-12337
- OpenAlex
- W4389523985
- Cited by
- 32
- References
- 57
- Citations by year
- 2026: 10 · 2025: 13 · 2024: 6 · 2023: 3
- FWCI
- 5.67
- 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)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- zero-shot evaluation, prompt engineering, large language models
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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