Capital Markets LLM Reliability Score: From Plausible to Bankable
Score
83提出了全新的七维评估框架，填补了金融领域LLM工作流输出可靠性评估的空白。
实验设计全面，包含多个模型、多个工作流和独立评审，分析深入且有具体数值支撑。
对金融机构评估和选型LLM有直接指导作用，可降低部署风险。
来自arXiv最新预印本，反映当前最前沿的研究动态。
来自知名预印本平台arXiv，论文结构规范，但尚未经过正式同行评审。
Base 92 × T2 Source weight ×0.9 = 83
Curation line 70Selected
Why selected
Introduces an innovative seven-dimension evaluation system with rigorous experimental design, directly guiding financial AI deployment.
AI commentary
「This paper is valuable for both finance and AI communities: it provides an actionable, workflow-oriented evaluation framework that's closer to real needs than generic benchmarks. If you're deploying LLMs in fintech or responsible for explainability/auditability, this is a must-read. The finding on decision usefulness is particularly striking—judges agree it's critical, yet models vary the most on it.」
Quick overview
Existing financial benchmarks evaluate QA-level accuracy but miss the 'bankability' required in real-world capital markets workflows. This paper introduces CM-LRS, assessing LLM outputs across seven dimensions: factual accuracy, evidence traceability, numerical consistency, workflow completeness, source discipline, decision usefulness, and auditability, each scored 0-5. Testing four models on five workflows, they find frontier closed-source models cluster within 0.22 points, while open-weight baselines lag significantly, especially on retrieval and synthesis tasks. Decision usefulness shows the widest cross-model dispersion and highest inter-judge agreement.
- CM-LRS evaluates LLM outputs in capital markets workflows across seven dimensions, prioritizing 'bankability' over surface fluency.
- Frontier closed-source models (Sonnet 4.6, Opus 4.7, GPT-5.5) score within 0.22 points, while Llama 3.3 70B lags significantly.
- The gap concentrates on retrieval (2.23-point gap) and synthesis (2.15) rather than extraction (0.84).
- Decision usefulness shows the widest cross-model dispersion (4.0 points on issuer profiling) and highest inter-judge agreement (mean r=0.52).
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