面向关键社会领域的大语言模型综述：金融、医疗与法律
A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law
AI总结：
本文综述了大语言模型在金融、医疗和法律三大高风险领域的方法、应用、挑战与伦理问题，强调跨学科合作与伦理警觉，并提供持续更新的阅读清单以推动未来研究。
摘要：
在快速发展的人工智能领域，GPT-3和GPT-4等大语言模型（LLMs）正在深刻变革金融、医疗和法律领域的面貌：这些领域以依赖专业知识、数据获取困难、高风险和严格的监管合规为特征。本综述详细探讨了大语言模型在这些高风险领域中的方法、应用、挑战和前瞻性机遇。我们强调了大语言模型在提升医疗诊断与治疗方法、创新金融分析以及完善法律解释与合规策略方面的关键作用。此外，我们批判性地审视了大语言模型在这些领域应用中的伦理问题，指出现有的伦理关切，以及构建尊重监管规范、透明、公平且稳健的AI系统的必要性。通过对现有文献和实际应用的全面回顾，我们展示了大语言模型的变革性影响，并阐明了跨学科合作、方法论进步和伦理警觉的迫切要求。通过这一视角，我们旨在激发对话并启发未来研究，致力于在这些依赖精确性的领域中最大化大语言模型的收益，同时降低其风险。为促进未来在这些关键社会领域中大语言模型的研究，我们还启动了一个阅读清单，追踪该主题下的最新进展，并将持续更新：\url{https://github.com/czyssrs/LLM_X_papers}。
英文摘要：
In the fast-evolving domain of artificial intelligence, large language models (LLMs) such as GPT-3 and GPT-4 are revolutionizing the landscapes of finance, healthcare, and law: domains characterized by their reliance on professional expertise, challenging data acquisition, high-stakes, and stringent regulatory compliance. This survey offers a detailed exploration of the methodologies, applications, challenges, and forward-looking opportunities of LLMs within these high-stakes sectors. We highlight the instrumental role of LLMs in enhancing diagnostic and treatment methodologies in healthcare, innovating financial analytics, and refining legal interpretation and compliance strategies. Moreover, we critically examine the ethics for LLM applications in these fields, pointing out the existing ethical concerns and the need for transparent, fair, and robust AI systems that respect regulatory norms. By presenting a thorough review of current literature and practical applications, we showcase the transformative impact of LLMs, and outline the imperative for interdisciplinary cooperation, methodological advancements, and ethical vigilance. Through this lens, we aim to spark dialogue and inspire future research dedicated to maximizing the benefits of LLMs while mitigating their risks in these precision-dependent sectors. To facilitate future research on LLMs in these critical societal domains, we also initiate a reading list that tracks the latest advancements under this topic, which will be continually updated: \url{https://github.com/czyssrs/LLM_X_papers}.
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。
