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--- |
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license: mit |
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language: |
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- en |
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metrics: |
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- accuracy |
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- bleu |
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pipeline_tag: table-question-answering |
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tags: |
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- code |
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--- |
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# TableDART Gating Network Checkpoint |
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This repository provides the trained gating network checkpoint for **TableDART: Dynamic Adaptive Multi-Modal Routing for Table Understanding**. |
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TableDART is a training-efficient framework that dynamically routes each table-query pair through the most appropriate reasoning path: Text-only, Image-only, or Fusion, while keeping all pretrained expert models **frozen**. |
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--- |
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## 🔍 Overview |
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Modeling semantic and structural information from tabular data remains a core challenge for effective table understanding. |
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Existing LLM-based approaches face several limitations: |
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- Table-as-Text methods flatten tables into text sequences, losing structural cues. |
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- Table-as-Image methods preserve layout but struggle with precise semantics. |
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- Static multimodal methods process all modalities for every query, introducing redundancy and potential cross-modal conflicts. |
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- Most approaches require expensive fine-tuning of large LLMs or multimodal models. |
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**Our Solution: TableDART** addresses these limitations through: |
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- Reusing pretrained single-modality expert models (kept frozen, plug-and-play) |
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- Learning only a lightweight 2.59M-parameter MLP gating network |
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- Dynamically selecting the optimal path for each table-query pair (instance-level) |
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- Introducing an LLM agent that mediates cross-modal knowledge integration when needed |
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This design avoids full LLM/MLLM fine-tuning, reduces computational redundancy, and maintains strong efficiency-performance trade-offs. |
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--- |
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## 🚀 Performance |
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Across 7 benchmarks, TableDART: |
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- Achieves state-of-the-art results on 4/7 benchmarks among open-source models |
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- Outperforms the strongest baseline by +4.02% accuracy on average |
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- Maintains significant computational efficiency gains |
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## 📦 What This Checkpoint Contains |
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This Hugging Face model includes: |
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- The trained MLP gating network checkpoint |
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⚠️ Note: This checkpoint does not include the pretrained text or image expert models. Please load those separately according to the official repository instructions. |
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--- |
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## 🛠 Code and Usage |
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Full training scripts, inference pipelines, and reproduction details are available at our Github Repository: https://github.com/xiaobo-xing/TableDART |
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--- |
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## 📄 Paper |
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ICLR 2026 OpenReview Version: |
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https://openreview.net/forum?id=4aZTiLH3fm |
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ArXiv Version: |
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https://arxiv.org/abs/2509.14671 |
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--- |
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## 📚 Citation |
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If you find TableDART helpful, please cite our paper and consider starring the repository. |
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### ICLR 2026 Version |
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```bibtex |
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@inproceedings{xing2026tabledart, |
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title={Table{DART}: Dynamic Adaptive Multi-Modal Routing for Table Understanding}, |
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author={Xiaobo Xing and Wei Yuan and Tong Chen and Quoc Viet Hung Nguyen and Xiangliang Zhang and Hongzhi Yin}, |
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booktitle={The Fourteenth International Conference on Learning Representations}, |
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year={2026}, |
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url={https://openreview.net/forum?id=4aZTiLH3fm} |
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} |
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``` |
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### ArXiv Version |
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```bibtex |
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@misc{xing2025tabledartdynamicadaptivemultimodal, |
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title={TableDART: Dynamic Adaptive Multi-Modal Routing for Table Understanding}, |
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author={Xiaobo Xing and Wei Yuan and Tong Chen and Quoc Viet Hung Nguyen and Xiangliang Zhang and Hongzhi Yin}, |
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year={2025}, |
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eprint={2509.14671}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2509.14671} |
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} |
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``` |
