Fin-RATE: A Real-world Financial Analytics and Tracking Evaluation Benchmark for LLMs on SEC Filings
arXiv:2602.07294v4 Announce Type: replace-cross Abstract: With the increasing deployment of Large Language Models (LLMs) in the finance domain, LLMs are increasingly expected to parse complex regulatory disclosures. However, existing benchmarks often focus on isolated details, failing to reflect the complexity of professional analysis that requires synthesizing information across multiple documents, reporting periods, and corporate entities. Furthermore, these benchmarks do not disentangle whether errors arise from retrieval failures, generation inaccuracies, domain-specific reasoning mistakes
The proliferation of LLMs into critical financial analysis necessitates robust and domain-specific evaluation benchmarks to ensure reliability and trust.
This benchmark addresses a critical gap in evaluating LLM performance in complex financial tasks, allowing for more confident and effective deployment in regulated industries.
The ability to accurately assess and improve financial LLMs will accelerate their integration into financial workflows, shifting how regulatory disclosures are analyzed.
- · Financial Technology Companies
- · Large Language Model Developers
- · Investment Firms
- · Underperforming LLM Developers
- · Manual Financial Analysts (eventually)
The Fin-RATE benchmark will become a standard for evaluating LLMs used in financial analysis.
Improved LLM performance in financial analytics could lead to more efficient markets and better risk assessment.
Enhanced trust in AI-driven financial analysis may accelerate regulatory acceptance of autonomous AI agents in finance.
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Read at arXiv cs.AI