CFMS: A Coarse-to-Fine Multimodal Synthesis Framework for Enhanced Tabular Reasoning
Summary: arXiv:2604.10973v1 Announce Type: new
Abstract
Reasoning over tabular data is a crucial capability for tasks like question answering and fact verification. It requires models to comprehend both free-form questions and semi-structured tables. While methods such as Chain-of-Thought (CoT) have introduced reasoning chains, purely symbolic methods are inherently limited by their inability to recognize holistic visual patterns. To address these challenges, we propose the Coarse-to-Fine Multimodal Synthesis framework (CFMS), a novel two-stage paradigm that hierarchically decouples high-level visual perception from granular symbolic reasoning.
The CFMS Framework
The CFMS framework operates in two distinct stages:
- Coarse Stage: In this initial phase, CFMS utilizes Multimodal Large Language Models (MLLMs) to synthesize a multi-perspective knowledge tuple. This synthesis is a one-time process that aggregates various viewpoints and information regarding the tabular data.
- Fine Stage: Following the Coarse Stage, the knowledge tuple serves as a dynamic reasoning map. In this stage, a symbolic engine carries out a targeted and efficient sequence of iterative operations over the table, refining the reasoning process.
Key Advantages of CFMS
CFMS is designed to enhance tabular reasoning capabilities in several significant ways:
- Holistic Understanding: By integrating MLLMs in the Coarse Stage, CFMS fosters a better understanding of the visual patterns present in the data.
- Dynamic Reasoning: The generated knowledge tuple acts as a flexible guide for the symbolic engine, allowing for more efficient and targeted reasoning processes.
- Robust Performance: Extensive experiments conducted on the WikiTQ and TabFact benchmarks indicate that CFMS achieves competitive accuracy compared to existing methods.
- Scalability: The framework exhibits robustness when handling large tables and demonstrates effectiveness even when instantiated with smaller backbone models, showcasing its generalizability across different contexts.
Experimental Results
To validate the efficacy of the CFMS framework, rigorous testing was carried out on established benchmarks such as WikiTQ and TabFact. The results highlighted that:
- CFMS consistently outperformed traditional symbolic methods, particularly in scenarios involving complex tabular data.
- The use of smaller backbone models did not compromise performance, which suggests that CFMS can be an effective solution even with limited computational resources.
Conclusion
The Coarse-to-Fine Multimodal Synthesis framework presents a significant advancement in the field of tabular reasoning. By effectively decoupling high-level visual perception from detailed symbolic reasoning, CFMS establishes a new paradigm that enhances the capability of AI systems to interpret and analyze tabular data. This framework not only improves accuracy but also ensures efficiency and scalability, making it a promising solution for future applications in AI-driven question answering and fact verification.