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Large Language Models (LLMs) have demonstrated their ability to replicatehuman behaviors across a wide range of scenarios. However, their capability inhandling complex, multi-character social interactions has yet to be fullyexplored, primarily due to the absence of robust, quantitative evaluationmethods. This gap has slowed the development of agents proficient in morenuanced interactions beyond simple exchanges, for example, small talk. Toaddress this challenge, we introduce the Multi-Agent Interaction EvaluationFramework (AntEval), encompassing a novel interaction framework and evaluationmethods. The interaction framework aims to foster an complex interactionenvironment that bolsters information exchange and intention expression withinsocial interactions. Furthermore, we introduce evaluation methods, includingtwo metrics: Information Exchanging Precision (IEP) and InteractionExpressiveness Gap (IEG), designed for the quantitative and objectiveassessment of agents' interaction competencies. Our findings highlight theutility of these evaluative methods and show significant potential forimproving LLMs' ability to construct agents that interact in a more naturalmanner with human-like intricacy.

AntEval: Evaluation of Social Interaction Competencies in LLM-Driven Agents