Selectively Answering Ambiguous Questions
Open accessAbstract
Trustworthy language models should abstain from answering questions when they do not know the answer.However, the answer to a question can be unknown for a variety of reasons.Prior research has focused on the case in which the question is clear and the answer is unambiguous but possibly unknown.But the answer to a question can also be unclear due to uncertainty of the questioner's intent or context.We investigate question answering from this perspective, focusing on answering a subset of questions with a high degree of accuracy, from a set of questions in which many are inherently ambiguous.In this setting, we find that the most reliable approach to decide when to abstain involves quantifying repetition within sampled model outputs, rather than the model's likelihood or self-verification as used in prior work.We find this to be the case across different types of uncertainty and model scales, and with or without instruction tuning.Our results suggest that sampling-based confidence scores help calibrate answers to relatively unambiguous questions, with more dramatic improvements on ambiguous questions.
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Recent work on LLM abstention explores techniques based on self-consistency, in which the model evaluates its own outputs and level of uncertainty before returning a response, demonstrating good performance in question-answering settings.
“In contrast, recent work on LLM abstention explores techniques based on self-consistency, in which the model evaluates its own outputs and level of uncertainty before returning a response, demonstrating good performance in question-answering settings [32, 70, 91, 133].”
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On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017) · arXiv · doi:10.48550/arxiv.1706.04599 · Cited by 3,435 · see citing works
PaLM: Scaling Language Modeling with Pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.02311 · Cited by 2,590 · see citing works
Natural Questions: A benchmark for question answering research
Kwiatkowski, T., Palomaki, J., Redfield, O., Collins, M., Parikh, A., Alberti, C., Epstein, D., Polosukhin, I., Devlin, J., Lee, K., et al. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00276 · Cited by 2,069 · see citing works
TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
Joshi, M., Choi, E., Weld, D., & Zettlemoyer, L. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1147 · Cited by 1,690 · see citing works
Scaling Instruction-Finetuned Language Models
Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, Y., Wang, X., Dehghani, M., Brahma, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2210.11416 · Cited by 1,441 · see citing works
The Curious Case of Neural Text Degeneration
Holtzman, A., Buys, J., Du, L., Forbes, M., & Choi, Y. (2019) · arXiv · doi:10.48550/arxiv.1904.09751 · Cited by 1,304 · see citing works
Hierarchical Neural Story Generation
Fan, A., Lewis, M., & Dauphin, Y. (2018) · arXiv · doi:10.48550/arxiv.1805.04833 · Cited by 1,242 · see citing works
Latent retrieval for weakly supervised open domain question answering
Lee, K., Chang, M.-W., & Toutanova, K. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1612 · Cited by 771 · see citing works
SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models
Manakul, P., Liusie, A., & Gales, M. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.557 · Cited by 557 · see citing works
Calibration of Pre-trained Transformers
Desai, S., & Durrett, G. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.21 · Cited by 209 · see citing works
Language Models (Mostly) Know What They Know
Kadavath, S., Conerly, T., Askell, A., Henighan, T., Drain, D., Perez, E., Schiefer, N., Hatfield-Dodds, Z., DasSarma, N., Tran-Johnson, E., et al. (2022) · arXiv · doi:10.48550/arxiv.2207.05221 · Cited by 187 · see citing works
Kuhn, L., Gal, Y., & Farquhar, S. (2023) · arXiv · doi:10.48550/arxiv.2302.09664 · Cited by 52 · see citing works
Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluation
Bulian, J., Buck, C., Gajewski, W., Börschinger, B., & Schuster, T. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.20 · Cited by 44 · see citing works
Do Language Models Know When They’re Hallucinating References?
Agrawal, A., Suzgun, M., Mackey, L., & Kalai, A. (2024) · Findings of the Association for Computational Linguistics: EACL 2024 · doi:10.18653/v1/2024.findings-eacl.62 · Cited by 37 · see citing works
Flagged first, then by citations
Research works printed but not linked: 29
- Aryaman Arora, Clara Meister, and Ryan Cotterell. 2022. Estimating the entropy of linguistic distri- butions. In Proceedings of the 60th Annual Meet- ing of the Association for Computational Linguistics (Volume 2: Short Papers), pages 175-195, Dublin, Ireland. Association for Computational Linguistics. · DOI
- Chi-Keung Chow. 1957. An optimum character recog- nition system using decision functions. IRE Transac- tions on Electronic Computers, EC-6(4):247-254.
- Li Dong, Chris Quirk, and Mirella Lapata. 2018. Con- fidence modeling for neural semantic parsing. In Proceedings of the 56th Annual Meeting of the As- sociation for Computational Linguistics (Volume 1: Long Papers), pages 743-753, Melbourne, Australia. Association for Computational Linguistics. · DOI
- Ran El-Yaniv et al. 2010. On the foundations of noise- free selective classification. Journal of Machine Learning Research, 11(5).
- Cornelia Gruber, Patrick Oliver Schenk, Malte Schier- holz, Frauke Kreuter, and Göran Kauermann. 2023. Sources of uncertainty in machine learning-a statisti- cians' view. arXiv e-prints, pages arXiv-2305.
- Abhyuday Jagannatha and Hong Yu. 2020. Calibrat- ing structured output predictors for natural language processing. In Proceedings of the 58th Annual Meet- ing of the Association for Computational Linguistics, pages 2078-2092, Online. Association for Computa- tional Linguistics. · DOI
- Zhengbao Jiang, Jun Araki, Haibo Ding, and Graham Neubig. 2021. How can we know when language models know? on the calibration of language models for question answering. Transactions of the Associa- tion for Computational Linguistics, 9:962-977. · DOI · Paper page
- Amita Kamath, Robin Jia, and Percy Liang. 2020. Se- lective question answering under domain shift. In Proceedings of the 58th Annual Meeting of the Asso- ciation for Computational Linguistics, pages 5684- 5696, Online. Association for Computational Lin- guistics. · DOI
- Lorenz Kuhn, Yarin Gal, and Sebastian Farquhar. 2022. Clam: Selective clarification for ambiguous ques- tions with large language models. arXiv preprint arXiv:2212.07769. · arXiv
- Volodymyr Kuleshov and Percy S Liang. 2015. Cali- brated structured prediction. In Advances in Neural Information Processing Systems, volume 28.
- Aviral Kumar and Sunita Sarawagi. 2019. Calibration of encoder decoder models for neural machine trans- lation. arXiv preprint arXiv:1903.00802. · arXiv
- Stephanie Lin, Jacob Hilton, and Owain Evans. 2022. Teaching models to express their uncertainty in words. arXiv preprint arXiv:2205.14334. · arXiv
- Zhen Lin, Shubhendu Trivedi, and Jimeng Sun. 2023. Generating with confidence: Uncertainty quantifi- cation for black-box large language models. arXiv e-prints, pages arXiv-2305.
- Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. 2022. Fantastically ordered prompts and where to find them: Overcoming few- shot prompt order sensitivity. In Proceedings of the 60th Annual Meeting of the Association for Compu- tational Linguistics (Volume 1: Long Papers), pages 8086-8098, Dublin, Ireland. Association for Compu- tational Linguistics. · DOI · Paper page
- Clara Meister, Afra Amini, Tim Vieira, and Ryan Cot- terell. 2021. Conditional Poisson stochastic beams. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 664- 681, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. · DOI
- Sewon Min, Julian Michael, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2020. AmbigQA: Answering am- biguous open-domain questions. In Proceedings of the 2020 Conference on Empirical Methods in Nat- ural Language Processing (EMNLP), pages 5783- 5797, Online. Association for Computational Lin- guistics. · DOI
- Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi, Xiuyuan Lu, and Benjamin Van Roy. 2021. Epistemic neural networks. arXiv preprint arXiv:2107.08924. · arXiv
- Jie Ren, Jiaming Luo, Yao Zhao, Kundan Krishna, Mo- hammad Saleh, Balaji Lakshminarayanan, and Pe- ter J Liu. 2023. Out-of-distribution detection and selective generation for conditional language mod- els. In The Eleventh International Conference on Learning Representations.
- Ivan Stelmakh, Yi Luan, Bhuwan Dhingra, and Ming- Wei Chang. 2022. ASQA: Factoid questions meet long-form answers. In Proceedings of the 2022 Con- ference on Empirical Methods in Natural Language Processing, pages 8273-8288, Abu Dhabi, United Arab Emirates. Association for Computational Lin- guistics.
- Neeraj Varshney and Chitta Baral. 2022. Model cas- cading: Towards jointly improving efficiency and accuracy of NLP systems. In Proceedings of the 2022 Conference on Empirical Methods in Natu- ral Language Processing, pages 11007-11021, Abu Dhabi, United Arab Emirates. Association for Com- putational Linguistics.
- Neeraj Varshney, Swaroop Mishra, and Chitta Baral. 2022. Investigating selective prediction approaches across several tasks in IID, OOD, and adversarial settings. In Findings of the Association for Compu- tational Linguistics: ACL 2022, pages 1995-2002, Dublin, Ireland. Association for Computational Lin- guistics. · DOI
- Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, and Denny Zhou. 2022. Self-consistency im- proves chain of thought reasoning in language mod- els. arXiv preprint arXiv:2203.11171. · arXiv
- Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed H. Chi, Quoc V Le, and Denny Zhou. 2022. Chain of thought prompt- ing elicits reasoning in large language models. In Advances in Neural Information Processing Systems.
- Allen R. Wilcox. 1973. Indices of qualitative variation and political measurement. The Western Political Quarterly, 26(2):325-343.
- John M Zelle and Raymond J Mooney. 1996. Learn- ing to parse database queries using inductive logic programming. In Proceedings of the National Con- ference on Artificial Intelligence, pages 1050-1055.
- Hugh Zhang, Daniel Duckworth, Daphne Ippolito, and Arvind Neelakantan. 2021a. Trading off diversity and quality in natural language generation. In Pro- ceedings of the Workshop on Human Evaluation of NLP Systems (HumEval), pages 25-33, Online. As- sociation for Computational Linguistics.
- Michael Zhang and Eunsol Choi. 2021. SituatedQA: In- corporating extra-linguistic contexts into QA. In Pro- ceedings of the 2021 Conference on Empirical Meth- ods in Natural Language Processing, pages 7371- 7387, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. · DOI
- Shujian Zhang, Chengyue Gong, and Eunsol Choi. 2021b. Knowing more about questions can help: Improving calibration in question answering. In Find- ings of the Association for Computational Linguis- tics: ACL-IJCNLP 2021, pages 1958-1970, Online. Association for Computational Linguistics. · DOI
- Kaitlyn Zhou, Dan Jurafsky, and Tatsunori Hashimoto. 2023. Navigating the grey area: Expressions of overconfidence and uncertainty in language models. arXiv preprint arXiv:2302.13439. · arXiv
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- Authors
- Cole, J., Zhang, M., Gillick, D., Eisenschlos, J., Dhingra, B., & Eisenstein, J.
- Affiliations
- Duke University (US)
- Venue
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Published
- 2023
- Pages
- 530-543
- OpenAlex
- W4389524305
- Cited by
- 19
- References
- 14
- Citations by year
- 2026: 6 · 2025: 8 · 2023: 5
- FWCI
- 3.37
- 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)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Keywords
- question answering, ambiguous questions, abstention, confidence calibration, instruction tuning
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