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arXiv Machine Learning By Kirill Borodin, Vasiliy Kudryavtsev, Ivan Viakhirev

The Null Token Knows: Reducing Message-Free Hallucination in ASR and NMT

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The paper investigates how encoder-decoder models in ASR and NMT can generate fluent text even when the input contains no recoverable message, a phenomenon known as message-free hallucination. By auditing the models’ reserved null tokens and manipulating their scores, the authors show that a higher null-token score can suppress fabrication but may also delete valid content or shorten translations. The study highlights that the null token can serve as a diagnostic tool for hallucination and suggests evaluating abstention methods by considering both suppression and deletion costs.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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