Thinking with Tables: Enhancing Multi-Modal Tabular Understanding via Neuro-Symbolic Reasoning
arXiv:2603.24004v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable reasoning capabilities across modalities such as images and text. However, tabular data, despite being a critical real-world modality, remains relatively underexplored in multimodal learning. In this paper, we focus on the task of Tabular-Vision Multi-Modal Understanding (TVMU) and identify three core challenges: (1) high structural variability and data incompleteness in tables, (2) implicit and complex feature dependencies, and (3) significant heterogeneity in problem-solving pipelines across downstream tasks. To address these issues, we propose Thinking with Tables (TWT). TWT employs a program-aided code-based neuro-symbolic reasoning mechanism that facilitates key operations, such as information extraction and element modeling, by interacting with external environments. We evaluate TWT on eight representative datasets. Experimental results demonstrate that TWT consi
arXiv:2603.24004v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable reasoning capabilities across modalities such as images and text. However, tabular data, despite being a critical real-world modality, remains relatively underexplored in multimodal learning. In this paper, we focus on the task of Tabular-Vision Multi-Modal Understanding (TVMU) and identify three core challenges: (1) high structural variability and data incompleteness in tables, (2) implicit and complex feature dependencies, and (3) significant heterogeneity in problem-solving pipelines across downstream tasks. To address these issues, we propose Thinking with Tables (TWT). TWT employs a program-aided code-based neuro-symbolic reasoning mechanism that facilitates key operations, such as information extraction and element modeling, by interacting with external environments. We evaluate TWT on eight representative datasets. Experimental results demonstrate that TWT consistently outperforms existing baselines by an average of 10\% in accuracy, achieving performance comparable to, or even surpassing, proprietary commercial SOTA LLMs on TVMU tasks. Models and codes are available at https://github.com/kunyang-YU/Thinking-with-Tables
Executive Summary
This paper proposes Thinking with Tables (TWT), a neuro-symbolic reasoning mechanism that addresses three core challenges in tabular data understanding: high structural variability, implicit feature dependencies, and heterogeneity in problem-solving pipelines. TWT employs a program-aided code-based approach that facilitates information extraction and element modeling by interacting with external environments. Experimental results show that TWT outperforms existing baselines by an average of 10% in accuracy, achieving performance comparable to or surpassing proprietary commercial LLMs. The proposed mechanism offers a promising solution for tabular-vision multi-modal understanding tasks, with potential applications in various industries. However, further evaluation and refinement are needed to fully explore its capabilities and limitations.
Key Points
- ▸ TWT addresses three core challenges in tabular data understanding
- ▸ TWT employs a program-aided code-based neuro-symbolic reasoning mechanism
- ▸ Experimental results show significant improvement over existing baselines
Merits
Strength in Addressing Complex Challenges
TWT offers a comprehensive solution to the three core challenges in tabular data understanding, making it a robust and effective mechanism for tabular-vision multi-modal understanding tasks.
High Accuracy and Performance
TWT achieves performance comparable to or surpassing proprietary commercial LLMs, demonstrating its potential for real-world applications.
Flexibility and Customizability
TWT's program-aided code-based approach allows for flexibility and customizability in information extraction and element modeling, making it adaptable to various industries and applications.
Demerits
Limited Evaluation and Refinement
Further evaluation and refinement of TWT are needed to fully explore its capabilities and limitations, particularly in real-world scenarios.
Dependence on External Environments
TWT's reliance on external environments for information extraction and element modeling may limit its applicability in scenarios where such environments are not available or accessible.
Scalability and Efficiency
TWT's performance and scalability may be affected by the complexity of the tabular data and the computational resources required for the program-aided code-based approach.
Expert Commentary
The proposed Thinking with Tables (TWT) mechanism offers a promising solution for tabular-vision multi-modal understanding tasks. By addressing the three core challenges in tabular data understanding, TWT demonstrates significant improvement over existing baselines. However, further evaluation and refinement are needed to fully explore its capabilities and limitations. The potential applications of TWT are vast, ranging from finance and healthcare to education and beyond. Its impact on the development of AI and machine learning technologies is also significant. As the field of multimodal learning and reasoning continues to evolve, TWT is a notable contribution that warrants further attention and investigation.
Recommendations
- ✓ Further evaluation and refinement of TWT in real-world scenarios
- ✓ Investigation of TWT's applicability in scenarios where external environments are not available or accessible
- ✓ Exploration of TWT's potential applications in various industries and its impact on data analysis and decision-making
Sources
Original: arXiv - cs.CL
