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Paper page - UCFE: A User-Centric Financial Expertise Benchmark for Large Language Models
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UCFE benchmark\nadopts a hybrid approach that combines human expert evaluations with dynamic,\ntask-specific interactions to simulate the complexities of evolving financial\nscenarios. Firstly, we conducted a user study involving 804 participants,\ncollecting their feedback on financial tasks. Secondly, based on this feedback,\nwe created our dataset that encompasses a wide range of user intents and\ninteractions. This dataset serves as the foundation for benchmarking 12 LLM\nservices using the LLM-as-Judge methodology. Our results show a significant\nalignment between benchmark scores and human preferences, with a Pearson\ncorrelation coefficient of 0.78, confirming the effectiveness of the UCFE\ndataset and our evaluation approach. UCFE benchmark not only reveals the\npotential of LLMs in the financial sector but also provides a robust framework\nfor assessing their performance and user satisfaction.The benchmark dataset and\nevaluation code are available.","upvotes":63,"discussionId":"6715fc2dac8cb216a41877db","githubRepo":"https://github.com/TobyYang7/UCFE-Benchmark","githubRepoAddedBy":"auto","ai_summary":"A user-centric framework evaluates LLMs in financial tasks through human feedback and task-specific interactions, achieving high correlation with human preferences.","ai_keywords":["Large language models (LLMs)","LLM-as-Judge methodology","User-Centric Financial Expertise (UCFE)","hybrid approach","human expert evaluations","task-specific interactions","user intentions","benchmark scores","Pearson correlation coefficient"],"githubStars":3},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"66cef20050bb4cbe31fc2247","avatarUrl":"/avatars/4c063b4caa059ae71232b72ff7c49ed5.svg","isPro":false,"fullname":"zhang","user":"stevzhang","type":"user"},{"_id":"66a7975b2f8a3f480a8f59b9","avatarUrl":"/avatars/bee7af9a76e157fbe98b9f9e529c3015.svg","isPro":false,"fullname":"Ming","user":"Karthusw","type":"user"},{"_id":"6715ff68d3505f282a191259","avatarUrl":"/avatars/163dafeb6e7adb5a33e7922d0466d190.svg","isPro":false,"fullname":"aoran","user":"cgar123","type":"user"},{"_id":"6716017031eba367c630b96b","avatarUrl":"/avatars/a6a12aa2e52320d2e260fc3cf6a39a56.svg","isPro":false,"fullname":"c","user":"long1231223","type":"user"},{"_id":"6716023c1f90c4c9c3a010f2","avatarUrl":"/avatars/4dc47951bd89a5b2986351ecd326369a.svg","isPro":false,"fullname":"houpeng xia","user":"riceshower11","type":"user"},{"_id":"671603bc002047ccd18e398f","avatarUrl":"/avatars/9f72f7ab10332ff2eb5e34517e124cd0.svg","isPro":false,"fullname":"RyanGu","user":"RyanGu","type":"user"},{"_id":"671604c5cea4db5528fe4aa1","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/KeADwm7JG0cYDRzDHfCRx.jpeg","isPro":false,"fullname":"yuankai66","user":"yuankai66","type":"user"},{"_id":"671607127a8f3964a9f5002a","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/dZsjY6CJKBwH97mzU_2s9.png","isPro":false,"fullname":"Xiao Cai","user":"largechestnut","type":"user"},{"_id":"639883cb11095028d87b78c1","avatarUrl":"/avatars/0bd2e430affd0a1a1a85a61a8394a438.svg","isPro":false,"fullname":"Melih Özcan","user":"staycoolish","type":"user"},{"_id":"6168218a4ed0b975c18f82a8","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/6168218a4ed0b975c18f82a8/vD4Q6KVcz5Td39QWTG-s7.png","isPro":true,"fullname":"NIONGOLO Chrys Fé-Marty","user":"Svngoku","type":"user"},{"_id":"620783f24e28382272337ba4","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/620783f24e28382272337ba4/zkUveQPNiDfYjgGhuFErj.jpeg","isPro":false,"fullname":"GuoLiangTang","user":"Tommy930","type":"user"},{"_id":"65fbdbc8fc9132a2dfd67c8f","avatarUrl":"/avatars/9e404fca5f7b53d49e1aa73d525f834d.svg","isPro":false,"fullname":"Minghao Wu","user":"magicc0nch","type":"user"}],"acceptLanguages":["*"],"dailyPaperRank":1}">
Papers
arxiv:2410.14059

UCFE: A User-Centric Financial Expertise Benchmark for Large Language Models

Published on Oct 17, 2024
· Submitted by
Yifei Zhang
on Oct 21, 2024
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Abstract

A user-centric framework evaluates LLMs in financial tasks through human feedback and task-specific interactions, achieving high correlation with human preferences.

AI-generated summary

This paper introduces the UCFE: User-Centric Financial Expertise benchmark, an innovative framework designed to evaluate the ability of large language models (LLMs) to handle complex real-world financial tasks. UCFE benchmark adopts a hybrid approach that combines human expert evaluations with dynamic, task-specific interactions to simulate the complexities of evolving financial scenarios. Firstly, we conducted a user study involving 804 participants, collecting their feedback on financial tasks. Secondly, based on this feedback, we created our dataset that encompasses a wide range of user intents and interactions. This dataset serves as the foundation for benchmarking 12 LLM services using the LLM-as-Judge methodology. Our results show a significant alignment between benchmark scores and human preferences, with a Pearson correlation coefficient of 0.78, confirming the effectiveness of the UCFE dataset and our evaluation approach. UCFE benchmark not only reveals the potential of LLMs in the financial sector but also provides a robust framework for assessing their performance and user satisfaction.The benchmark dataset and evaluation code are available.

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