![[2602.07294] Fin-RATE: A Real-world Financial Analytics and Tracking Evaluation Benchmark for LLMs on SEC Filings](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.07294] Fin-RATE: A Real-world Financial Analytics and Tracking Evaluation Benchmark for LLMs on SEC Filings
Summary
The paper introduces Fin-RATE, a benchmark for evaluating Large Language Models (LLMs) on SEC filings, addressing the limitations of existing benchmarks in financial analysis.
Why It Matters
As LLMs are increasingly utilized in finance, effective evaluation benchmarks are crucial for ensuring their accuracy in parsing complex regulatory documents. Fin-RATE aims to improve the assessment of LLMs by reflecting real-world financial analyst workflows, thus enhancing the reliability of AI applications in finance.
Key Takeaways
- Fin-RATE benchmarks LLMs on SEC filings, simulating financial analyst tasks.
- Existing benchmarks fail to capture the complexities of multi-document analysis.
- Performance of LLMs declines significantly with more complex tasks, highlighting limitations.
- The benchmark categorizes errors to better diagnose performance issues.
- Results indicate a need for improved training and evaluation methods in financial AI.
Computer Science > Computational Engineering, Finance, and Science arXiv:2602.07294 (cs) [Submitted on 7 Feb 2026 (v1), last revised 14 Feb 2026 (this version, v3)] Title:Fin-RATE: A Real-world Financial Analytics and Tracking Evaluation Benchmark for LLMs on SEC Filings Authors:Yidong Jiang, Junrong Chen, Eftychia Makri, Jialin Chen, Peiwen Li, Ali Maatouk, Leandros Tassiulas, Eliot Brenner, Bing Xiang, Rex Ying View a PDF of the paper titled Fin-RATE: A Real-world Financial Analytics and Tracking Evaluation Benchmark for LLMs on SEC Filings, by Yidong Jiang and 9 other authors View PDF HTML (experimental) 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, or misinterpretation of the query or context, making it difficult to precisely diagnose performance bottlenecks. To bridge these gaps, we introduce Fin-RATE, a benchmark built on U.S. Securities and Exchange Commission (SEC) filings and mirroring financial analyst workflows through three pathways: detail-oriented reasoning with...
