Empirical study finds verbalized per-token confidence methods in LLMs for MT perform similarly to internal signals on error detection and calibration but show little correlation.
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A survey that organizes LLMs-as-judges research into functionality, methodology, applications, meta-evaluation, and limitations.
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Speaking in Self-Assessing Tongues: On the Verbalized Confidence of LLMs in Machine Translation
Empirical study finds verbalized per-token confidence methods in LLMs for MT perform similarly to internal signals on error detection and calibration but show little correlation.
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LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods
A survey that organizes LLMs-as-judges research into functionality, methodology, applications, meta-evaluation, and limitations.