LLM能成为优秀的财务顾问吗？：面向优化结果的个人决策初步研究
Can LLMs be Good Financial Advisors?: An Initial Study in Personal Decision Making for Optimized Outcomes
AI总结：
本文通过13个多语言个人金融问题测试ChatGPT与Bard，发现LLM输出虽流畅合理，但在提供准确可靠的金融建议方面仍有关键缺陷。
摘要：
功能日益强大的基于大型语言模型（LLM）的聊天机器人（如ChatGPT和Bard）正逐渐面向用户开放，它们有可能彻底改变公众的决策质量。在此背景下，我们着手研究此类系统在个人金融领域的表现，而普惠金融数十年来一直是银行宣称的总体目标。我们提出了13个代表个人金融中银行产品的问题，涉及银行账户、信用卡和定期存单及其产品间交互，以及与高额购买、银行欠款支付和投资建议相关的决策，并使用不同方言和语言（英语、非裔美国人白话英语和泰卢固语）进行测试。我们发现，尽管这些聊天机器人的输出流畅且看似合理，但在利用基于LLM的聊天机器人提供准确、可靠的金融信息方面仍存在关键差距。
英文摘要：
Increasingly powerful Large Language Model (LLM) based chatbots, like ChatGPT and Bard, are becoming available to users that have the potential to revolutionize the quality of decision-making achieved by the public. In this context, we set out to investigate how such systems perform in the personal finance domain, where financial inclusion has been an overarching stated aim of banks for decades. We asked 13 questions representing banking products in personal finance: bank account, credit card, and certificate of deposits and their inter-product interactions, and decisions related to high-value purchases, payment of bank dues, and investment advice, and in different dialects and languages (English, African American Vernacular English, and Telugu). We find that although the outputs of the chatbots are fluent and plausible, there are still critical gaps in providing accurate and reliable financial information using LLM-based chatbots.
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。
