Speaking in Self-Assessing Tongues: On the Verbalized Confidence of LLMs in Machine Translation

arXiv:2606.17234v1 Announce Type: new Abstract: The rapid rise in popularity of large language models (LLMs) for translation calls for a thorough study of the reliability of their confidence in their own outputs. Unlike many generation tasks, translation errors and confidence levels can be useful at different levels of granularity (tokens, words, or spans). Unsupervised approaches based on internal signals like predicted probabilities can be misleading because they reflect certainty among alternatives rather than correctness. In addition, they require access to such internal signals. Here, we

Source: arXiv cs.CL — read the full report at the original publisher.

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