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A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law

2024-05-02 22:43:02
Zhiyu Zoey Chen, Jing Ma, Xinlu Zhang, Nan Hao, An Yan, Armineh Nourbakhsh, Xianjun Yang, Julian McAuley, Linda Petzold, William Yang Wang

Abstract

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{this https URL}.

Abstract (translated)

在人工智能快速发展的领域,如金融、医疗和法律等领域,大型语言模型(LLMs)如GPT-3和GPT-4正在改变这些领域的格局:这些领域以依赖专业知识、具有挑战性的数据收集、高风险和高监管合规而闻名。这项调查详细探讨了LLMs在这些高风险领域中的方法论、应用、挑战和未来展望。我们强调LLM在提高医疗保健中的诊断和治疗方法、创新金融分析和优化法律解释和合规策略中的关键作用。此外,我们对这些领域中LLM应用的伦理问题进行了批判性分析,指出存在的伦理担忧以及需要透明、公正和强大的AI系统来尊重监管规范。通过全面回顾现有文献和实际应用,我们展示了LLM的变革性影响,并强调了跨学科合作、方法和伦理警惕的重要性。通过这一视角,我们旨在激发对话,并激励未来研究,最大限度地利用LLM的优势,同时减轻其风险在这些精准依赖的领域。为了促进未来关于LLM在这些关键社会领域的研究,我们还启动了一个跟踪最新进展的阅读列表,该列表将不断更新:\url{this <https://url.org>}.

URL

https://arxiv.org/abs/2405.01769

PDF

https://arxiv.org/pdf/2405.01769.pdf


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