Tag
The article discusses why AI systems have difficulty interpreting uncertainty and ambiguity in human conversation, highlighting ongoing challenges in natural language understanding.
The author argues that current AI excels at processing transcript language but misses non-verbal cues like hesitation and tone, highlighting a gap between understanding language and understanding human communication.
This paper investigates asymmetries in LLMs' pragmatic competence by comparing their performance as judges of linguistic appropriateness versus as generators of pragmatically appropriate language. The study finds that many models perform substantially better as pragmatic listeners than as speakers, suggesting misalignment between evaluation and generation capabilities.