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1
Detecting Hallucinated Content in Conditional Neural Sequence Generation ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Diab, Mona
;
Ghazvininejad, Marjan
;
Gu, Jiatao
;
Guzman, Francisco
;
Neubig, Graham
;
Zettlemoyer, Luke
;
Zhou, Chunting
. - : Underline Science Inc., 2021
Abstract:
Read paper: https://www.aclanthology.org/2021.findings-acl.120 Abstract: Neural sequence models can generate highly fluent sentences, but recent studies have also shown that they are also prone to hallucinate additional content not supported by the input. This variety of fluent but wrong text is particularly problematic, as it will not be possible for users to tell they are being presented incorrect content. To detect these errors, we propose a task to predict whether each token in the output sequence is hallucinated (not contained in the input) and collect new manually annotated evaluation sets for this task. We also introduce a novel method for learning to model hallucination detection, using pretrained language models fine tuned on synthetic data that includes automatically inserted hallucinations. Experiments on machine translation and abstractive text summarization demonstrate the effectiveness of our proposed approach --- we consistently outperform strong baselines across all the benchmark datasets. ...
Keyword:
Computational Linguistics
;
Condensed Matter Physics
;
Deep Learning
;
Electromagnetism
;
FOS Physical sciences
;
Information and Knowledge Engineering
;
Neural Network
;
Semantics
URL:
https://underline.io/lecture/26211-detecting-hallucinated-content-in-conditional-neural-sequence-generation
https://dx.doi.org/10.48448/4s6r-5381
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