(A)我不是律师，但……：联合法律专家制定负责任的大语言模型法律咨询政策
(A)I Am Not a Lawyer, But...: Engaging Legal Experts towards Responsible LLM Policies for Legal Advice
AI总结：
本研究联合20位法律专家，通过案例研讨方法梳理LLM提供法律咨询的情境考量，提出四维框架与回复策略，补充了现有研究忽视的法律考量，为制定负责任的LLM法律领域政策提供依据。
摘要：
大语言模型（LLM）在包括法律咨询在内的众多专业领域为用户提供建议的能力日益增强。然而，由于法律领域要求极高的专业知识，且相关建议可能带来现实后果，依赖LLM处理法律查询引发了诸多担忧。为探究LLM应当及不应当向用户提供建议的时机与原因，我们采用受案例推理启发的方法，与20位法律专家共同开展了工作坊。我们提供了真实的查询（“案例”），让专家们得以研究具体场景下的细粒度问题，以及全局性的技术与法律约束，最终为LLM开发者整理出一套具体的情境考量因素。通过综合影响LLM回复恰当性的各类因素，我们提出了一个包含4个维度的框架：（1）用户属性与行为，（2）查询性质，（3）AI能力，（4）社会影响。我们分享了专家针对LLM回复策略的建议，核心是帮助用户找到“正确的提问方向”及相关信息，而非给出确定性的法律判断。我们的研究发现了现有文献中被忽视的全新法律考量，比如未经授权的法律执业、保密性，以及错误建议的责任问题。基于案例的研讨方法帮助我们获取了细粒度、源自实践的洞见，其质量远超脱离语境的调研或推测性原则所能产出的成果。这些发现凸显了我们的方法可用于将特定领域的专业知识与实践转化为政策，引导LLM行为朝着更负责任的方向发展。
英文摘要：
Large language models (LLMs) are increasingly capable of providing users with advice in a wide range of professional domains, including legal advice. However, relying on LLMs for legal queries raises concerns due to the significant expertise required and the potential real-world consequences of the advice. To explore \textit{when} and \textit{why} LLMs should or should not provide advice to users, we conducted workshops with 20 legal experts using methods inspired by case-based reasoning. The provided realistic queries ("cases") allowed experts to examine granular, situation-specific concerns and overarching technical and legal constraints, producing a concrete set of contextual considerations for LLM developers. By synthesizing the factors that impacted LLM response appropriateness, we present a 4-dimension framework: (1) User attributes and behaviors, (2) Nature of queries, (3) AI capabilities, and (4) Social impacts. We share experts' recommendations for LLM response strategies, which center around helping users identify `right questions to ask' and relevant information rather than providing definitive legal judgments. Our findings reveal novel legal considerations, such as unauthorized practice of law, confidentiality, and liability for inaccurate advice, that have been overlooked in the literature. The case-based deliberation method enabled us to elicit fine-grained, practice-informed insights that surpass those from de-contextualized surveys or speculative principles. These findings underscore the applicability of our method for translating domain-specific professional knowledge and practices into policies that can guide LLM behavior in a more responsible direction.
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。
