AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive Scenarios
On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks
Abstract (arXiv)
Large language models (LLMs) are increasingly leveraged to empower autonomous agents to simulate human beings in various fields of behavioral research. However, evaluating their capacity to navigate complex social interactions remains a challenge. Previous studies face limitations due to insufficient scenario diversity, complexity, and a single-perspective focus. To this end, we introduce AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive Scenarios. Drawing on Dramaturgical Theory, AgentSense employs a bottom-up approach to create 1,225 diverse social scenarios constructed from extensive scripts. We evaluate LLM-driven agents through multi-turn interactions, emphasizing both goal completion and implicit reasoning. We analyze goals using ERG theory and conduct comprehensive experiments. Our findings highlight that LLMs struggle with goals in complex social scenarios, especially high-level growth needs, and even GPT-4o requires improvement in private information reasoning. Code and data are available at urlhttps://github.com/ljcleo/agentsense.
Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 23 of its 61 references that are in the map. First posted on arXiv 25 Oct 2024.
Builds on
23 of 61 references.
Closest ancestors · 2
Same direction, newest first
- 2024Towards Objectively Benchmarking Social Intelligence for Language Agents at Action Level8 Apr
- 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society31 Mar
Other references · 21
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Citing papers per year: 2024: 2; 2025: 17; 2026: 18.
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