EQ-Negotiator：动态情感人格赋能小型语言模型进行边缘部署的信用谈判
EQ-Negotiator: Dynamic Emotional Personas Empower Small Language Models for Edge-Deployable Credit Negotiation
AI总结：
本文提出EQ-Negotiator框架，通过整合博弈论与隐马尔可夫模型，使小型语言模型具备动态情感人格，提升边缘设备上的信用谈判效率与道德标准。
摘要：
大型语言模型（LLMs）在自动化谈判中的部署设定了高性能基准，但其计算成本和数据隐私要求使其不适合许多隐私敏感的设备端应用，如移动助手、具身AI代理或私人客户交互。尽管小型语言模型（SLMs）提供了实用的替代方案，但其在扮演情感复杂的复杂人格方面表现逊色，尤其是在信用谈判中。本文介绍EQ-Negotiator，一种新颖的框架，通过情感人格弥合这一能力差距。其核心是一个推理系统，整合博弈论与隐马尔可夫模型（HMM）以在线学习和跟踪债务人情感状态，无需预训练。这使EQ-Negotiator能够为SLMs提供战略智能，以对抗操纵、缓解冲突并维护道德标准。通过广泛的大规模代理到代理模拟，涵盖多样化的信用谈判场景，包括对抗性债务人策略如欺诈、威胁和扮演受害者，我们表明，一个7B参数语言模型配备EQ-Negotiator在债务回收和谈判效率方面优于基准LLMs，其规模超过10倍。这项工作将人格建模从描述性人物档案推进到在隐私约束下运行的动态情感架构。此外，本文证明了战略情感智能，而非原始模型规模，是自动化谈判成功的关键因素，为有效、道德且隐私保护的AI谈判者铺平了道路，这些谈判者可以在边缘设备上运行。
英文摘要：
The deployment of large language models (LLMs) in automated negotiation has set a high performance benchmark, but their computational cost and data privacy requirements render them unsuitable for many privacy-sensitive, on-device applications such as mobile assistants, embodied AI agents or private client interactions. While small language models (SLMs) offer a practical alternative, they suffer from a significant performance gap compared to LLMs in playing emotionally charged complex personas, especially for credit negotiation. This paper introduces EQ-Negotiator, a novel framework that bridges this capability gap using emotional personas. Its core is a reasoning system that integrates game theory with a Hidden Markov Model(HMM) to learn and track debtor emotional states online, without pre-training. This allows EQ-Negotiator to equip SLMs with the strategic intelligence to counter manipulation while de-escalating conflict and upholding ethical standards. Through extensive agent-to-agent simulations across diverse credit negotiation scenarios, including adversarial debtor strategies like cheating, threatening, and playing the victim, we show that a 7B parameter language model with EQ-Negotiator achieves better debt recovery and negotiation efficiency than baseline LLMs more than 10 times its size. This work advances persona modeling from descriptive character profiles to dynamic emotional architectures that operate within privacy constraints. Besides, this paper establishes that strategic emotional intelligence, not raw model scale, is the critical factor for success in automated negotiation, paving the way for effective, ethical, and privacy-preserving AI negotiators that can operate on the edge.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
