Unveiling the Limits of Large Language Models in Inferring Pragmatic Meaning from Non-Verbal Responses
arXiv:2606.01845v1 Announce Type: new Abstract: Although large language models (LLMs) have shown considerable progress in pragmatic language understanding, prior research has focused mainly on their comprehension of verbal behavior. Nonetheless, non-verbal behavior remains a fundamental component of human communication, especially when deliberately utilized in isolation to convey indirect meanings. In this work, we present the first systematic evaluation of LLMs' ability to infer pragmatic meaning in dialogue consisting solely of non-verbal responses. We explore three research questions: (1) C
This research emerges as LLMs demonstrate advanced verbal comprehension, prompting a deeper exploration into their limitations regarding non-verbal communication, a critical frontier for human-like AI.
Understanding LLMs' ability to interpret non-verbal cues is crucial for developing truly intelligent and empathetic AI, extending their utility beyond text-based interactions into richer human-computer communication.
This work begins to map the boundaries of current LLM capabilities, highlighting a significant area for future research and development in AI for embodied agents and complex human environments.
- · AI researchers in pragmatics
- · Developers of embodied AI
- · Multimodal AI platforms
- · LLM developers ignoring non-verbal communication
- · AI applications requiring nuanced human interaction
The immediate effect is a clearer understanding of the performance gap in LLMs concerning non-verbal pragmatic inference.
This understanding will drive increased investment and research into multimodal AI systems that integrate non-verbal data streams.
Long-term, this could lead to more sophisticated and context-aware AI agents capable of navigating complex social interactions, blurring lines between human and machine interpretation.
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