FinAutoRubric: Expert-Guided Automatic Rubric Generation for Evaluating Financial Research Agents

Problem

The paper addresses a significant gap in the evaluation of financial research agents, specifically the need for expert-guided rubrics that can systematically assess their performance. Existing methods lack the rigor and specificity required for nuanced evaluation in the finance domain. This work is particularly relevant as it is presented as a preprint and has not undergone peer review.

Method

The authors propose an architecture for expert-guided automatic rubric generation, which includes a query-specific rubric generation process. The method involves a review and validation mechanism to ensure the generated rubrics align with expert evaluations. The core dataset utilized is the 100-query FinAutoRubric Benchmark, which encompasses 78 tasks across eight asset classes. A notable component of the system is the Task Bank, which contains reusable criteria for rubric generation. However, the paper does not disclose specific details regarding the training compute used in the development of this system.

Results

The results indicate that the generated rubrics closely track expert scoring when compared to the strongest evaluated generator, although no specific baseline is provided for this comparison. Additionally, the rubrics demonstrate a high level of agreement with human grading, reinforcing their validity. In blind reviews conducted by in-house analysts, the generated rubrics were preferred, although no quantitative metrics were reported to substantiate this preference.

Limitations

The authors identify several limitations in their approach. First, the fixed nature of the per-item rubrics makes them costly to extend, which could hinder adaptability in dynamic evaluation contexts. Second, the system does not accommodate the encoding of individual institutions' specific standards, potentially limiting its applicability across diverse financial environments.

Why it matters

The implications of this work are significant for downstream applications in financial research evaluation. By providing a structured and expert-informed rubric generation process, FinAutoRubric can enhance the reliability and consistency of assessments for financial research agents. This advancement could lead to improved decision-making in finance, fostering better research practices and outcomes.