The Art of Abstention: Selective Prediction and Error Regularization for Natural Language Processing
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Ji Xin, Raphael Tang, Yaoliang Yu, Jimmy Lin. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.
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LongMemEval assesses five core memory abilities: information extraction, multi-session reasoning, temporal reasoning, knowledge updates, and abstention.
“The benchmark assesses five core memory abilities: information extraction (Singh, 2018; Peng et al., 2024), multisession reasoning, temporal reasoning, knowledge updates, and abstention (Xin et al., 2021).”
From Hippocampus: An Efficient and Scalable Memory Module for Agentic AI (2026) · cites this paper as Xin et al., 2021
Recent NLP work develops the two-axis view for question answering, classification, and calibrated LLM uncertainty.
“recent NLP work develops the same view for question answering (Kamath et al., 2020), classification (Xin et al., 2021), and calibrated LLM uncertainty (Kadavath et al., 2022).”
From Agreement Metrics for LLM-as-Judge Evaluation: What to Report and Why (2026) · cites this paper as Xin et al., 2021
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works
Transformers: State-of-the-Art Natural Language Processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2020.emnlp-demos.6 · Cited by 8,741 · see citing works
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y., & Ghahramani, Z. (2015) · arXiv · doi:10.48550/arxiv.1506.02142 · Cited by 6,471 · see citing works
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
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
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Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph
Vashurin, R., Fadeeva, E., Vazhentsev, A., Rvanova, L., Vasilev, D., Tsvigun, A., Petrakov, S., Xing, R., Sadallah, A., Grishchenkov, K., et al. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00737 · Cited by 21 · see citing works
LM-Polygraph: Uncertainty estimation for language models
Fadeeva, E., Vashurin, R., Tsvigun, A., Vazhentsev, A., Petrakov, S., Fedyanin, K., Vasilev, D., Goncharova, E., Panchenko, A., Panov, M., et al. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2023.emnlp-demo.41 · Cited by 20 · see citing works
Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs
Chen, J., Yoon, J., Ebrahimi, S., Arik, S., Pfister, T., & Jha, S. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.345 · Cited by 13 · see citing works
Hippocampus: An Efficient and Scalable Memory Module for Agentic AI
Li, Y., Cao, L., Ahmed, F., Sharma, P., & Li, B. (2026) · arXiv · doi:10.48550/arxiv.2602.13594
“The benchmark assesses five core memory abilities: information extraction (Singh, 2018; Peng et al., 2024), multi-session reasoning, temporal reasoning, knowledge updates, and abstention (Xin et al., 2021).”
- Authors
- Xin, J., Tang, R., Yu, Y., & Lin, J.
- Affiliations
- University of Waterloo (CA)
- Venue
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Published
- 2021
- Pages
- 1040-1051
- OpenAlex
- W3173618889
- Cited by
- 45
- References
- 5
- Citations by year
- 2026: 9 · 2025: 6 · 2024: 6 · 2023: 15 · 2022: 8 · 2021: 1
- FWCI
- 3.46
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Funders
- Canada First Research Excellence Fund
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- abstention, selective prediction, prediction confidence, uncertainty estimation, natural language processing
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