为金融领域大模型评估风险提供系统性方法
A Methodology for Assessing the Risk of Metric Failure in LLMs Within the Financial Domain
- 构建风险评估框架，识别指标失效的潜在因素
- 指出传统指标在金融场景中泛化能力差
- 适合金融AI落地项目中的评估与决策
随着生成式人工智能在金融服务行业的应用，衡量模型性能成为主要障碍。历史机器学习指标往往无法泛化到生成式AI任务，常需结合领域专家（SME）评估。然而，即便如此，许多项目仍未能充分考虑特定指标选择带来的独特风险。此外，由基础研究实验室和教育机构创建的广泛基准，也难以适应工业实际应用。本文阐明了这些挑战，并提出一种风险评估框架，以更有效地应用SME评估与机器学习指标。
原文摘要 · Abstract (English)
As Generative Artificial Intelligence is adopted across the financial services industry, a significant barrier to adoption and usage is measuring model performance. Historical machine learning metrics can oftentimes fail to generalize to GenAI workloads and are often supplemented using Subject Matter Expert (SME) Evaluation. Even in this combination, many projects fail to account for various unique risks present in choosing specific metrics. Additionally, many widespread benchmarks created by foundational research labs and educational institutions fail to generalize to industrial use. This paper explains these challenges and provides a Risk Assessment Framework to allow for better application of SME and machine learning Metrics
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