Towards Objectively Benchmarking Social Intelligence for Language Agents at Action Level
This addresses the problem of evaluating social intelligence in language agents for AI researchers, though it is incremental as it builds on existing simulation-based approaches.
The authors tackled the lack of realistic and objective benchmarks for social intelligence in language agents by introducing the Social Tasks in Sandbox Simulation (STSS) benchmark, which assesses agents at the action level in multi-agent simulations, and found it challenging for state-of-the-art agents while effectively discriminating between them.
Prominent large language models have exhibited human-level performance in many domains, even enabling the derived agents to simulate human and social interactions. While practical works have substantiated the practicability of grounding language agents in sandbox simulation or embodied simulators, current social intelligence benchmarks either stay at the language level or use subjective metrics. In pursuit of a more realistic and objective evaluation, we introduce the Social Tasks in Sandbox Simulation (STSS) benchmark, which assesses language agents \textbf{objectively} at the \textbf{action level} by scrutinizing the goal achievements within the multi-agent simulation. Additionally, we sample conversation scenarios to build a language-level benchmark to provide an economically prudent preliminary evaluation and align with prevailing benchmarks. To gauge the significance of agent architecture, we implement a target-driven planning (TDP) module as an adjunct to the existing agent. Our evaluative findings highlight that the STSS benchmark is challenging for state-of-the-art language agents. Furthermore, it effectively discriminates between distinct language agents, suggesting its usefulness as a benchmark for evaluating both language models and agent architectures.