标题： 生成语言模型中的对抗谈判动态 显示英文标题

标题： Adversarial Negotiation Dynamics in Generative Language Models

摘要： 生成式语言模型越来越多地被用于合同起草和改进，从而形成了一个竞争双方部署不同语言模型相互对抗的情景。 这不仅引入了博弈论的挑战，还带来了与人工智能安全性和安全性相关的重要问题，因为对方使用的语言模型可能是未知的。 这些竞争性交互可以被视为对抗性测试平台，在这些平台上，模型实际上被红队攻击以暴露其漏洞，例如生成带有偏见、有害或法律问题的文本。 尽管这些挑战的重要性毋庸置疑，但这些模型在对抗环境中的竞争稳健性和安全性仍知之甚少。 在这项小型研究中，我们通过评估主要开源语言模型在一对一竞赛中的性能和漏洞来解决这个问题，模拟现实世界的合同谈判。 我们进一步探讨了这些对抗性交互如何揭示潜在风险，从而为开发更安全可靠的模型提供信息。 我们的发现有助于扩大人工智能安全领域的研究，为竞争性法律环境中模型选择和优化提供了见解，并为减轻风险提供了可行策略。 显示英文摘要

摘要： Generative language models are increasingly used for contract drafting and enhancement, creating a scenario where competing parties deploy different language models against each other. This introduces not only a game-theory challenge but also significant concerns related to AI safety and security, as the language model employed by the opposing party can be unknown. These competitive interactions can be seen as adversarial testing grounds, where models are effectively red-teamed to expose vulnerabilities such as generating biased, harmful or legally problematic text. Despite the importance of these challenges, the competitive robustness and safety of these models in adversarial settings remain poorly understood. In this small study, we approach this problem by evaluating the performance and vulnerabilities of major open-source language models in head-to-head competitions, simulating real-world contract negotiations. We further explore how these adversarial interactions can reveal potential risks, informing the development of more secure and reliable models. Our findings contribute to the growing body of research on AI safety, offering insights into model selection and optimisation in competitive legal contexts and providing actionable strategies for mitigating risks.