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Evidence record 6123 · automatically gathered

Evaluating Large Language Models in a Complex Hidden Role Game

Quantifying the deceptive potential of Large Language Models (LLMs) is critical for AI safety, yet difficult to achieve in uncontrolled environments. This work investigates the reasoning, persuasion, and deceptive capabilities of LLMs within the social deduction game Secret Hitler. I introduce an open-source framework and novel metrics to measure performance: Role Identification Accuracy, Deception Retention Rate, and Game State Impact Rate. By benchmarking models against rule-based algorithms a

Record details

Published: 9 April 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Environment · Finance, VC & PE
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (9 April 2026), “Evaluating Large Language Models in a Complex Hidden Role Game,” evidence record 6123, https://ethics.ai/record/6123 (originally published by arXiv).

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