Papers › Open FinLLM Leaderboard: Towards Financial AI Readiness
Open FinLLM Leaderboard: Towards Financial AI Readiness
Shengyuan Colin Lin, Felix Tian, Keyi Wang, Xingjian Zhao, Jimin Huang, Qianqian Xie, Luca Borella, Matt White, Christina Dan Wang, Kairong Xiao, Xiao-Yang Liu Yanglet, Li Deng
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Financial large language models (FinLLMs) with multimodal capabilities are envisioned to revolutionize applications across business, finance, accounting, and auditing. However, real-world adoption requires robust benchmarks of FinLLMs' and FinAgents' performance. Maintaining an open leaderboard is crucial for encouraging innovative adoption and improving model effectiveness. In collaboration with Linux Foundation and Hugging Face, we create an open FinLLM leaderboard, which serves as an open platform for assessing and comparing AI models' performance on a wide spectrum of financial tasks. By demoncratizing access to advances of financial knowledge and intelligence, a chatbot or agent may enhance the analytical capabilities of the general public to a professional level within a few months of usage. This open leaderboard welcomes contributions from academia, open-source community, industry, and stakeholders. In particular, we encourage contributions of new datasets, tasks, and models for continual update. Through fostering a collaborative and open ecosystem, we seek to promote financial AI readiness.
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