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This paper evaluates the abilities of large language models (LLMs) and multimodal LLMs for addressee detection, turn-change prediction, and next speaker prediction in multi-party meeting conversations. Results show text-based LLMs outperform supervised models and humans in next speaker prediction, while multimodal LLMs improve over text-only models in other tasks but remain below human performance.
GroupMemBench is a new benchmark for evaluating LLM agent memory in multi-party conversations, exposing failures in current memory systems with the best achieving only 46% average accuracy.