Year: 2025
Type: conference-paper
Abstract: Solving financial problems demands complex reasoning, multimodal data processing, and a broad technical understanding, presenting unique challenges for current large language models (LLMs).We introduce XFINBENCH, a novel benchmark with 4,235 examples designed to evaluate LLM's ability in solving compleX, knowledge-intensive Financial problems across diverse graduate-level finance topics with multi-modal context.We identify five core capabilities of LLMs using XFINBENCH, i.e, terminology understanding, temporal reasoning, future forecasting, scenario planning, and numerical modelling.Upon XFINBENCH, we conduct extensive experiments on 18 leading models.The result shows that o1 is the best-performing text-only model with an overall accuracy of 67.3%, but still lags significantly behind human experts with 12.5%, especially in temporal reasoning and scenario planning capabilities.We further construct a knowledge bank with 3,032 finance terms for knowledge augmentation analysis, and find that relevant knowledge to the question only brings consistent accuracy improvements to small opensource model.Additionally, our error analysis reveals that rounding errors during calculation and blindness to position and intersection of curves in the image are two primary issues leading to model's poor performance in calculating and visual-context questions, respectively.1
Authors: Zhihan Zhang, Yixin Cao, Lizi Liao
Institutions: Singapore Management University, Fudan University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Awards: G2342870495, G6854926366
OpenAlex ID: W4412888535