Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·Jun 12·5 min read
Fin-RATE: A Real-world Financial Analytics and Tracking Evaluation Benchmark for LLMs on SEC Filings
TL;DR
Existing benchmarks for Large Language Models (LLMs) in finance fail to capture the complexity of analyzing SEC filings. Fin-RATE was developed as a comprehensive evaluation benchmark that reflects real-world financial analyst workflows.
✦ Why It Matters
Engineers can leverage Fin-RATE to better evaluate and improve LLMs for financial document analysis.
Key Takeaways
- Utilize Fin-RATE for comprehensive LLM evaluation in finance.
- Focus on improving LLM performance in cross-entity comparisons.
- Address hallucinations and mismatches in temporal data.
- Develop benchmarks that reflect real-world financial analysis workflows.
Full Summary
As LLMs are increasingly used in finance, they must effectively analyze complex regulatory documents like SEC filings. Current benchmarks often assess isolated details, neglecting the multifaceted nature of financial analysis that involves synthesizing information across various documents and time periods.
Fin-RATE was created to address this gap, simulating financial analyst workflows through three pathways: detailed reasoning within individual disclosures, cross-entity comparisons, and tracking a firm's performance over time. The evaluation involved 17 LLMs, including both open-source and finance-specialized models, under different contexts.
Results indicated a notable accuracy decline of 18.60% and 14.35% when tasks shifted from single-document analysis to longitudinal and cross-entity evaluations. This performance degradation was linked to increased hallucinations, mismatches in temporal and entity data, and declines in reasoning quality.
These findings underscore the need for more nuanced benchmarks that can better diagnose LLM performance issues in financial contexts.