arXiv:2601.14569v2 Announce Type: replace Abstract: Social understanding abilities are crucial for multimodal large language models (MLLMs) to interpret human social interactions. We introduce SOCIAL CAPTION, a framework grounded in interaction theory to evaluate social understanding abilities of MLLMs along three dimensions: Social Inference (SI), the ability to make accurate inferences about interactions; Holistic Social Analysis (HSA), the ability to generate comprehensive descriptions of interactions; Directed Social Analysis (DSA), the ability to generate relevant information from interac
The rapid advancement of MLLMs necessitates more sophisticated evaluation frameworks as these models move towards real-world social interaction. This specific development addresses a critical gap in assessing social understanding, which is key to broader AI application. The timing reflects the current pace of MLLM development and the growing demand for explainable and reliable AI.
Evaluating social understanding is crucial for the safe and effective deployment of AI in human-centric applications, affecting areas from customer service to autonomous decision-making. The ability for MLLMs to interpret human social interactions accurately will accelerate enterprise adoption of autonomous systems, moving beyond current narrow AI capabilities to more human-like integration, ultimately leading to greater societal acceptance. This framework provides a standardized method to bench
The introduction of SOCIAL CAPTION provides a structured framework for MLLMs to interpret human social interactions. It allows for more precise measurement of AI understanding, which impacts future model development and deployment.
- · AI developers
- · Multimodal LLMs
- · Researchers in AI ethics
- · SaaS companies leveraging MLLMs
- · Companies with socially inept AI
- · Legacy AI evaluation methods
- · Developers ignoring social understanding
Improved MLLMs with enhanced social understanding capabilities will be developed at a faster rate.
More sophisticated and nuanced AI agents will emerge, capable of navigating complex human social dynamics in diverse applications.
Increased societal trust in AI systems could accelerate the integration of AI into sensitive domains, potentially altering human-system interaction paradigms.
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