Fine-Grained and Thematic Evaluation of LLMs in Social Deduction Game

Abstract: Recent studies have investigated whether LLMs can support obscure communication that requires specialized skills, such as inferring subtext or doublespeak. To conduct the investigation, researchers have used social deduction games (SDGs) as their experimental environment, in which players conceal and infer specific information. However, prior work has often overlooked how LLMs should be evaluated in such settings. Specifically, we point out two issues with the evaluation methods they employed. First, metrics used in prior studies are coarse-grained as they are based on overall game outcomes that often fail to capture event-level behaviors; Second, error analyses have lacked structured methodologies capable of producing insights that meaningfully support evaluation outcomes. To address these issues, we propose a macroscopic and systematic approach to the investigation. Specifically, we introduce seven fine-grained metrics that resolve the first issue. To tackle the second issue, we conducted a thematic analysis and identified four major reasoning failures that undermine LLMs' performance in obscured communication.