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Open FinLLM Leaderboard: Towards Financial AI Readiness
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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.
Forward citations
Cited by 2 Pith papers
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Interpretable LLMs for Credit Risk: A Systematic Review and Taxonomy
A systematic review and taxonomy that organizes LLM-based credit risk research by model architecture, data modality, explainability mechanism, and application domain.
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Towards Unified Multimodal Financial Forecasting: Integrating Sentiment Embeddings and Market Indicators via Cross-Modal Attention
The abstract claims a multimodal stock predictor beats numeric-only baselines, but the body text is an unrelated topology note, leaving the claim unsupported.
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