TART – An Open-Source Tool-Augmented Framework for Explainable Table-Based Reasoning

TART is designed to overcome the limitations of LLMs in table reasoning by combining structured table processing, precise tool execution, and explainability. The key components of TART are:

1. Table Formatter: Prepares tables by formatting, aligning, and cleaning the data for structured input.
2. Tool Maker: Dynamically generates tools (Python functions) tailored for complex table operations.
3. Explanation Generator: Integrates structured reasoning steps and tool outputs into human-readable explanations.

TART is evaluated on nine table-based reasoning benchmarks, including SCITAB, TabFact, FinQA, and HybridQA. The framework consistently outperforms Chain-of-Thought (CoT) prompting by integrating structured tool-assisted reasoning.

TART has received the Best Paper Runner-Up Award at the 3rd Table Representation Learning Workshop at NeurIPS 2024 and has been accepted at NAACL 2025. Its code and dataset are publicly available for further research. Researchers working on LLM explainability, table reasoning, and AI-assisted fact-checking are encouraged to contribute.