量化大语言模型的风险倾向：通过角色扮演进行伦理聚焦与偏见检测
Quantifying Risk Propensities of Large Language Models: Ethical Focus and Bias Detection through Role-Play
AI总结：
本研究将DOSPERT量表应用于大语言模型，提出EDRAS伦理风险态度量表，并结合角色扮演方法量化评估多个主流LLMs的风险倾向及对不同群体的系统性偏见，为构建更公平可信的AI系统提供工具。
摘要：
随着大语言模型（LLMs）日益普及，对其安全性、伦理性和潜在偏见的担忧也在上升。系统评估LLMs的风险决策倾向和态度，尤其是在伦理领域，已变得至关重要。本研究创新性地将认知科学中的领域特定风险承担（DOSPERT）量表应用于LLMs，并提出一种新颖的伦理决策风险态度量表（EDRAS），以深入评估LLMs的伦理风险态度。我们进一步提出一种整合风险量表与角色扮演的新方法，用于定量评估LLMs中的系统性偏见。通过对多个主流LLMs的系统评估与分析，我们评估了LLMs在多个领域的“风险人格”，特别关注伦理领域，并揭示和量化了LLMs对不同群体的系统性偏见。本研究有助于理解LLMs的风险决策，并确保其安全可靠的应用。我们的方法为识别和缓解偏见提供了工具，有助于构建更公平、更可信的AI系统。代码和数据已公开。
英文摘要：
As Large Language Models (LLMs) become more prevalent, concerns about their safety, ethics, and potential biases have risen. Systematically evaluating LLMs' risk decision-making tendencies and attitudes, particularly in the ethical domain, has become crucial. This study innovatively applies the Domain-Specific Risk-Taking (DOSPERT) scale from cognitive science to LLMs and proposes a novel Ethical Decision-Making Risk Attitude Scale (EDRAS) to assess LLMs' ethical risk attitudes in depth. We further propose a novel approach integrating risk scales and role-playing to quantitatively evaluate systematic biases in LLMs. Through systematic evaluation and analysis of multiple mainstream LLMs, we assessed the "risk personalities" of LLMs across multiple domains, with a particular focus on the ethical domain, and revealed and quantified LLMs' systematic biases towards different groups. This research helps understand LLMs' risk decision-making and ensure their safe and reliable application. Our approach provides a tool for identifying and mitigating biases, contributing to fairer and more trustworthy AI systems. The code and data are available.
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。
