Hongyang Yang, Boyu Zhang, Neng Wang, Cheng Guo, Xiaoli Zhang, Likun Lin, Junlin Wang, Tianyu Zhou, Mao Guan, Runjia Zhang, Christina Dan Wang
{"title":"FinRobot:使用大型语言模型的金融应用开源人工智能代理平台","authors":"Hongyang Yang, Boyu Zhang, Neng Wang, Cheng Guo, Xiaoli Zhang, Likun Lin, Junlin Wang, Tianyu Zhou, Mao Guan, Runjia Zhang, Christina Dan Wang","doi":"arxiv-2405.14767","DOIUrl":null,"url":null,"abstract":"As financial institutions and professionals increasingly incorporate Large\nLanguage Models (LLMs) into their workflows, substantial barriers, including\nproprietary data and specialized knowledge, persist between the finance sector\nand the AI community. These challenges impede the AI community's ability to\nenhance financial tasks effectively. Acknowledging financial analysis's\ncritical role, we aim to devise financial-specialized LLM-based toolchains and\ndemocratize access to them through open-source initiatives, promoting wider AI\nadoption in financial decision-making. In this paper, we introduce FinRobot, a novel open-source AI agent platform\nsupporting multiple financially specialized AI agents, each powered by LLM.\nSpecifically, the platform consists of four major layers: 1) the Financial AI\nAgents layer that formulates Financial Chain-of-Thought (CoT) by breaking\nsophisticated financial problems down into logical sequences; 2) the Financial\nLLM Algorithms layer dynamically configures appropriate model application\nstrategies for specific tasks; 3) the LLMOps and DataOps layer produces\naccurate models by applying training/fine-tuning techniques and using\ntask-relevant data; 4) the Multi-source LLM Foundation Models layer that\nintegrates various LLMs and enables the above layers to access them directly.\nFinally, FinRobot provides hands-on for both professional-grade analysts and\nlaypersons to utilize powerful AI techniques for advanced financial analysis.\nWe open-source FinRobot at\n\\url{https://github.com/AI4Finance-Foundation/FinRobot}.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"44 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"FinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Models\",\"authors\":\"Hongyang Yang, Boyu Zhang, Neng Wang, Cheng Guo, Xiaoli Zhang, Likun Lin, Junlin Wang, Tianyu Zhou, Mao Guan, Runjia Zhang, Christina Dan Wang\",\"doi\":\"arxiv-2405.14767\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As financial institutions and professionals increasingly incorporate Large\\nLanguage Models (LLMs) into their workflows, substantial barriers, including\\nproprietary data and specialized knowledge, persist between the finance sector\\nand the AI community. These challenges impede the AI community's ability to\\nenhance financial tasks effectively. Acknowledging financial analysis's\\ncritical role, we aim to devise financial-specialized LLM-based toolchains and\\ndemocratize access to them through open-source initiatives, promoting wider AI\\nadoption in financial decision-making. 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FinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Models
As financial institutions and professionals increasingly incorporate Large
Language Models (LLMs) into their workflows, substantial barriers, including
proprietary data and specialized knowledge, persist between the finance sector
and the AI community. These challenges impede the AI community's ability to
enhance financial tasks effectively. Acknowledging financial analysis's
critical role, we aim to devise financial-specialized LLM-based toolchains and
democratize access to them through open-source initiatives, promoting wider AI
adoption in financial decision-making. In this paper, we introduce FinRobot, a novel open-source AI agent platform
supporting multiple financially specialized AI agents, each powered by LLM.
Specifically, the platform consists of four major layers: 1) the Financial AI
Agents layer that formulates Financial Chain-of-Thought (CoT) by breaking
sophisticated financial problems down into logical sequences; 2) the Financial
LLM Algorithms layer dynamically configures appropriate model application
strategies for specific tasks; 3) the LLMOps and DataOps layer produces
accurate models by applying training/fine-tuning techniques and using
task-relevant data; 4) the Multi-source LLM Foundation Models layer that
integrates various LLMs and enables the above layers to access them directly.
Finally, FinRobot provides hands-on for both professional-grade analysts and
laypersons to utilize powerful AI techniques for advanced financial analysis.
We open-source FinRobot at
\url{https://github.com/AI4Finance-Foundation/FinRobot}.