AREG：对抗性资源提取游戏用于评估大型语言模型的说服力与抗性
AREG: Adversarial Resource Extraction Game for Evaluating Persuasion and Resistance in Large Language Models
AI总结：
AREG通过对抗性资源提取游戏评估大型语言模型的说服与抗性能力，发现两者弱相关且存在系统性防御优势。
摘要：
评估大型语言模型（LLMs）的社会智能越来越需要超越静态文本生成，转向动态、对抗性的互动。我们介绍了对抗性资源提取游戏（AREG），这是一个基准测试，将说服和抗性定义为多轮零和谈判中的财务资源争夺。通过在前沿模型上进行轮换锦标赛，AREG能够在单一互动框架内联合评估进攻性（说服）和防御性（抗性）能力。我们的分析提供了证据，表明这些能力弱相关（ρ=0.33）且实证上分离：强大的说服表现并不总是预测强大的抗性，反之亦然。在所有评估的模型中，抗性得分超过说服得分，表明在对抗对话设置中存在系统性的防御优势。进一步的语言分析表明，互动结构在这些结果中起着核心作用。逐步的承诺寻求策略与更高的提取成功率相关，而验证寻求响应在成功的防御中比明确拒绝更常见。这些发现表明，LLMs中的社会影响并非单一能力，专注于说服的评估框架可能忽视不对称的行为漏洞。
英文摘要：
Evaluating the social intelligence of Large Language Models (LLMs) increasingly requires moving beyond static text generation toward dynamic, adversarial interaction. We introduce the Adversarial Resource Extraction Game (AREG), a benchmark that operationalizes persuasion and resistance as a multi-turn, zero-sum negotiation over financial resources. Using a round-robin tournament across frontier models, AREG enables joint evaluation of offensive (persuasion) and defensive (resistance) capabilities within a single interactional framework. Our analysis provides evidence that these capabilities are weakly correlated ($ρ= 0.33$) and empirically dissociated: strong persuasive performance does not reliably predict strong resistance, and vice versa. Across all evaluated models, resistance scores exceed persuasion scores, indicating a systematic defensive advantage in adversarial dialogue settings. Further linguistic analysis suggests that interaction structure plays a central role in these outcomes. Incremental commitment-seeking strategies are associated with higher extraction success, while verification-seeking responses are more prevalent in successful defenses than explicit refusal. Together, these findings indicate that social influence in LLMs is not a monolithic capability and that evaluation frameworks focusing on persuasion alone may overlook asymmetric behavioral vulnerabilities.
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