FCMR: Robust Evaluation of Financial Cross-Modal Multi-Hop Reasoning
This work addresses the need for robust benchmarks to assess multimodal reasoning in financial decision-making, though it is incremental as it builds on existing evaluation frameworks.
The paper tackles the problem of insufficient evaluation of multimodal large language models' multi-hop reasoning across diverse sources by introducing the Financial Cross-Modal Multi-Hop Reasoning (FCMR) benchmark, which reveals that even state-of-the-art models like Claude 3.5 Sonnet achieve only 30.4% accuracy on the most challenging tier.
Real-world decision-making often requires integrating and reasoning over information from multiple modalities. While recent multimodal large language models (MLLMs) have shown promise in such tasks, their ability to perform multi-hop reasoning across diverse sources remains insufficiently evaluated. Existing benchmarks, such as MMQA, face challenges due to (1) data contamination and (2) a lack of complex queries that necessitate operations across more than two modalities, hindering accurate performance assessment. To address this, we present Financial Cross-Modal Multi-Hop Reasoning (FCMR), a benchmark created to analyze the reasoning capabilities of MLLMs by urging them to combine information from textual reports, tables, and charts within the financial domain. FCMR is categorized into three difficulty levels-Easy, Medium, and Hard-facilitating a step-by-step evaluation. In particular, problems at the Hard level require precise cross-modal three-hop reasoning and are designed to prevent the disregard of any modality. Experiments on this new benchmark reveal that even state-of-the-art MLLMs struggle, with the best-performing model (Claude 3.5 Sonnet) achieving only 30.4% accuracy on the most challenging tier. We also conduct analysis to provide insights into the inner workings of the models, including the discovery of a critical bottleneck in the information retrieval phase.