Mutual Information Alleviates Hallucinations in Abstractive Summarization

Liam van der Poel, Ryan Cotterell, Clara Meister


Abstract
Despite significant progress in the quality of language generated from abstractive summarization models, these models still exhibit the tendency to hallucinate, i.e., output content not supported by the source document. A number of works have tried to fix—or at least uncover the source of—the problem with limited success. In this paper, we identify a simple criterion under which models are significantly more likely to assign more probability to hallucinated content during generation: high model uncertainty. This finding offers a potential explanation for hallucinations: models default to favoring text with high marginal probability, i.e., high-frequency occurrences in the training set, when uncertain about a continuation. It also motivates possible routes for real-time intervention during decoding to prevent such hallucinations. We propose a decoding strategy that switches to optimizing for pointwise mutual information of the source and target token—rather than purely the probability of the target token—when the model exhibits uncertainty. Experiments on the dataset show that our method decreases the probability of hallucinated tokens while maintaining the Rouge and BERT-S scores of top-performing decoding strategies.
Anthology ID:
2022.emnlp-main.399
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5956–5965
Language:
URL:
https://aclanthology.org/2022.emnlp-main.399
DOI:
10.18653/v1/2022.emnlp-main.399
Bibkey:
Cite (ACL):
Liam van der Poel, Ryan Cotterell, and Clara Meister. 2022. Mutual Information Alleviates Hallucinations in Abstractive Summarization. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 5956–5965, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Mutual Information Alleviates Hallucinations in Abstractive Summarization (van der Poel et al., EMNLP 2022)
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PDF:
https://aclanthology.org/2022.emnlp-main.399.pdf