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Calibrating LLM Judges for Human and AI Conversations
This work evaluates state-of-the-art LLMs as pointwise and pairwise judges of conversational success on CANDOR, finding pointwise scoring correlates moderately with human ratings, while pairwise comparison suffers from long transcripts and positional bias.
Why We Need Speech to Evaluate Speech Translation
It is argued that progress requires dedicated speech-specific training data and models that genuinely condition on speech, and both text- and speech-based quality estimation metrics on two contrastive datasets targeting gender agreement and prosody fall short.