InvestPhilBench: A Multi-Layer Benchmark for Evaluating Large Language Model Procedural Reasoning in Expert Investment Philosophy

2023-11-28 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Capital Markets & Investment Management · Depth: Expert, extended

Summary

InvestPhilBench is a multi-layer dynamic benchmark designed to evaluate large language models' procedural reasoning in expert investment philosophy. The v0.6 release includes 118 primary-source-verified investment principle cards, 25 decision framework cards with explicit topology metadata, and 243 QA questions (197 development, 46 held-out test), featuring 8 expert-difficulty questions. For scalable scoring, the benchmark introduces the Automated Scoring Pipeline (BASP) with five algorithmic metrics (OGRS, KCCS, SAP@k, IVP, CKCA), a Failure Mode Detection Protocol (FMDP) for six failure modes, and Gate Reconstruction Accuracy (GRA). A preliminary four-model sanity wave on the 188-question development split revealed a sharp provider-tier split (BASP 0.906 ↔ 0.438). Crucially, the BASP composite saturates at the frontier (Claude L4 = 0.932), masking a procedural deficit that GRA exposes (frontier L4 GRA ≈ 0.77, L7 GRA 0.57–0.62). The automated BASP composite also aligns with human expert ratings at Pearson r = 0.72 (MAE = 0.10).

Key takeaway

For MLOps Engineers deploying LLMs in financial analysis, recognize that composite evaluation scores can mask critical procedural reasoning failures. Your models might generate fluent prose but omit crucial kill criteria or misapply sequential decision logic. Prioritize evaluation using granular metrics like Gate Reconstruction Accuracy (GRA) to expose these deficits. Implement targeted Retrieval-Augmented Generation (RAG) and Framework-Aware Chain-of-Thought prompting to improve procedural fidelity and mitigate financial risk from flawed investment recommendations.

Key insights

Large language models exhibit a significant procedural reasoning deficit in expert investment philosophy, often masked by fluent output.

Principles

• LLMs' procedural knowledge encoding from prose is weaker than computational or factual recall.
• Composite evaluation metrics can saturate, hiding underlying procedural reasoning gaps.
• Dynamic benchmarking with scenario perturbation is crucial for contamination resistance.

Method

InvestPhilBench evaluates LLMs across an eight-layer cognitive taxonomy using Gold Reasoning Programs (GRPs) for dual accuracy. It employs the BASP with five algorithmic metrics and FMDP for failure mode detection, across three conditions (closed-book, RAG, oracle).

In practice

• Targeted RAG with top-3 principle cards can be more effective than full framework injection.
• Framework-Aware Chain-of-Thought prompting improves multi-step procedural task accuracy.
• Process supervision during training can help close the procedural application gap.

Topics

• Investment Philosophy
• Large Language Models
• Financial NLP
• LLM Benchmarking
• Procedural Reasoning
• Automated Evaluation
• Retrieval-Augmented Generation

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.