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On Exposure Bias, Hallucination and Domain Shift in Neural Machine Translation
Open accessAbstract
The standard training algorithm in neural machine translation (NMT) suffers from exposure bias, and alternative algorithms have been proposed to mitigate this.However, the practical impact of exposure bias is under debate.In this paper, we link exposure bias to another well-known problem in NMT, namely the tendency to generate hallucinations under domain shift.In experiments on three datasets with multiple test domains, we show that exposure bias is partially to blame for hallucinations, and that training with Minimum Risk Training, which avoids exposure bias, can mitigate this.Our analysis explains why exposure bias is more problematic under domain shift, and also links exposure bias to the beam search problem, i.e. performance deterioration with increasing beam size.Our results provide a new justification for methods that reduce exposure bias: even if they do not increase performance on in-domain test sets, they can increase model robustness to domain shift.
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“Further, in longform tasks consisting of generating multiple sentences or paragraphs, the hallucination problem can be exacerbated due to the issue of exposure bias (Wang & Sennrich, 2020).”
From Chain-of-Verification Reduces Hallucination in Large Language Models (2023) · cites this paper as Wang & Sennrich, 2020
In the citing paper
“Durmus et al. (2020) and Wang et al. (2020) suggested using QG and QA to identify factual inconsistencies in abstractive summaries, which is more Precisionoriented.”
From Q2: : Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering (2021) · cites this paper as Wang et al. (2020)
In the citing paper
“Their approach is based on the intuition that if a summary is consistent with its source, questions asked on the summary and the source should result in similar answers.”
From Q2: : Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering (2021) · cites this paper as Wang et al. (2020)
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Self-citationEnhancing the Transformer Decoder with Transition-based Syntax
Choshen, L., & Abend, O. (2022) · Conference on Computational Natural Language Learning · doi:10.18653/v1/2022.conll-1.27 · Cited by 6
Inverse Reinforcement Learning for Text Summarization
Fu, Y., Xiong, D., & Dong, Y. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.436 · Cited by 4
SURF: Semantic-level Unsupervised Reward Function for Machine Translation
Anuchitanukul, A., & Ive, J. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.334 · Cited by 5
Few-shot LLM Synthetic Data with Distribution Matching
Ren, J., Du, Z., Wen, Z., Jia, Q., Dai, S., Wu, C., & Dong, Z. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3701716.3715245 · Cited by 7
Adaptor: Objective-Centric Adaptation Framework for Language Models
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Fang, X., Yuan, H. R., Li, H., Kou, J., Gu, C., Zhang, W., Duan, X., & Fang, Y. (2024) · International Conference on Universal Village (UV) · doi:10.1109/uv63228.2024.11189217 · Cited by 4
Provilkov, I., & Malinin, A. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.677 · Cited by 5
Lan, Z., Li, W., Su, J., Xiao, X., Liu, J., Wu, W., & Lyu, Y. (2023) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.14267 · Cited by 3
Transition based Graph Decoder for Neural Machine Translation.Preprint
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Identifying large language model hallucinations in health communication
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Open-World Factually Consistent Question Generation
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Chatgpt Inaccuracy Mitigation During Technical Report Understanding: Are we There Yet?
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Lin, H., Yang, B., Yao, L., Liu, D., Zhang, H., Xie, J., Zhang, M., & Su, J. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.200 · Cited by 2
Quantifying Exposure Bias for Open-ended Language GenerationPreprint
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Metadata
- Authors
- Wang, C., & Sennrich, R.
- Affiliations
- University of Edinburgh (GB)
- University of Zurich (CH)
- Corresponding author
- Sennrich, Rico
- Venue
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Published
- 2020
- Pages
- 3544-3552
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W3035214886
- Cited by
- 118
- References
- 36
- Citations by year
- 2026: 7 · 2025: 14 · 2024: 11 · 2023: 31 · 2022: 22 · 2021: 28 · 2020: 5
- FWCI
- 8.21
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- Funders
- National Science Foundation
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (176727)
- Samsung
- Engineering and Physical Sciences Research Council (EP/S001271/1)
- 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 Recognition and Synthesis
- Keywords
- exposure bias, hallucinations, domain shift, neural machine translation, beam search, spot size, model robustness, training algorithms
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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