---
title: "RAG over Tables — paper by Zou et al. (2026)"
description: "The authors propose T-RAG, a retrieval framework built for large table collections. 66 references, cited by 3 papers here."
authors: ["Jiaru Zou", "Dongqi Fu", "Sirui Chen", "Xinrui He", "Zihao Li", "Yada Zhu", "Jiawei Han", "Jingrui He"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.1902/"
doi: "10.18653/v1/2026.findings-acl.1902"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zou-2026-rag-over-tables-hierarchical-memory/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/zou-2026-rag-over-tables-hierarchical-memory/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/6/) / RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarking

# RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarking

**Authors:** [Jiaru Zou](https://md.research.elamur.ai/free/en/authors/jiaru-zou/), Dongqi Fu, [Sirui Chen](https://md.research.elamur.ai/free/en/authors/sirui-chen/), Xinrui He, [Zihao Li](https://md.research.elamur.ai/free/en/authors/zihao-li/), [Yada Zhu](https://md.research.elamur.ai/free/en/authors/yada-zhu/), [Jiawei Han](https://md.research.elamur.ai/free/en/authors/jiawei-han/) and [Jingrui He](https://md.research.elamur.ai/free/en/authors/jingrui-he/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.1902/) · [PDF](https://aclanthology.org/2026.findings-acl.1902.pdf) · [DOI 10.18653/v1/2026.findings-acl.1902](https://doi.org/10.18653/v1/2026.findings-acl.1902) · [OpenAlex](https://openalex.org/W7166841028)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [3](https://md.research.elamur.ai/free/en/papers/zou-2026-rag-over-tables-hierarchical-memory/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Many real questions can only be answered by combining facts spread over several tables, yet RAG systems mostly target plain text, and realistic benchmarks for multi-table retrieval are scarce. The authors propose T-RAG, a retrieval framework built for large table collections. Each table is linearized from its caption and headers and described by three feature views: semantic embeddings, spaCy-based structural statistics, and TF-IDF. KMeans clustering is run separately on each view, and the resulting clusters serve as different kinds of hyperedges in a heterogeneous hypergraph. At query time, a coarse stage keeps only representative tables from each cluster. A fine stage then scores a subgraph with PageRank-style ranking, and the selected tables are passed to the LLM in a graph-aware arrangement. The authors also release MultiTableQA, a benchmark assembled from real-world sources. T-RAG outperforms table retrieval, RAG, and table-to-graph baselines on accuracy and recall while keeping latency competitive. It also improves answer quality across several backbone LLMs, which makes it a practical option for QA over databases or web tables.

## Key points
- MultiTableQA has 57,193 tables and 23,758 questions across three tasks: table fact verification (TFV), single-hop TQA, and multi-hop TQA.
- Retrieval gains reach up to 9.4% over prior methods. Downstream QA improves by about 11.8% on average across LLMs such as Qwen-2.5, Claude-3.5, and GPT-4o (the abstract's figure); the main text reports 11.2% against the strongest baselines.
- The coarse stage removes about 87–88% of candidate tables, and the fine stage reduces the pool to the final 10 (e.g., 34,351 → 4,204 → 10 for TFV).
- On Spider with GPT-4o, T-RAG scores 31.9 Accuracy@10, 42.3 Recall@10, and 73.9 EM@10, beating TAPAS (25.4 / 38.6 / 71.1), Table-E5, and DTR.
- Code and data are available at github.com/jiaruzouu/T-RAG. The authors name GNNs such as GAT and LLM fine-tuning as future directions.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Meta](https://md.research.elamur.ai/free/en/companies/meta/) — Company, Menlo Park, United States · 183 works · 228,558 citations
- [University of Illinois Urbana-Champaign](https://md.research.elamur.ai/free/en/universities/university-of-illinois-urbana-champaign/) — University, Urbana, United States · 184 works · 69,372 citations
- [IBM](https://md.research.elamur.ai/free/en/companies/ibm/) — Company, Armonk, United States · 139 works · 70,691 citations

📚 Shelves (6): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 2,869 works · [🗄️ Information Systems](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · 1,437 works

🧭 Topics (1): [🔍 Information retrieval & search](https://md.research.elamur.ai/free/en/topics/information-retrieval/) · 473 works

## Cite

Cite as: Jiaru Zou, Dongqi Fu, Sirui Chen, Xinrui He, Zihao Li, Yada Zhu, Jiawei Han and Jingrui He. 2026. RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarking. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zou2026rag,
  title = {{RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarking}},
  author = {Jiaru Zou, Dongqi Fu, Sirui Chen, Xinrui He, Zihao Li, Yada Zhu, Jiawei Han and Jingrui He},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1902},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1902}
}
```

- [← previous Qwen-Scope: Turning Sparse Features into Development Tools for Large Language Models](https://md.research.elamur.ai/free/en/papers/deng-2026-qwen-scope-turning-sparse/)
- [next → ReflectiveRAG: Rethinking Adaptivity in Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/verma-2026-reflectiverag-rethinking-adaptivity-retrieval-augmented/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/6/)

## References

66 references, 34 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Marah Abdin, Jyoti Aneja, Hany Awadalla, Ahmed Awadallah, Ammar Ahmad Awan, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Jianmin Bao, Harkirat Behl, and 1 others. 2024. [Phi-3 technical report: A highly capable language model locally on your phone](https://md.research.elamur.ai/free/en/papers/abdin-2024-phi-3-technical-report-highly/). arXiv preprint arXiv:2404.14219.
2. Garima Agrawal, Tharindu Kumarage, Zeyad Alghamdi, and Huan Liu. 2024. Can knowledge graphs reduce hallucinations in llms? : A survey. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Pap… [find](https://scholar.google.com/scholar?q=Garima+Agrawal%2C+Tharindu+Kumarage%2C+Zeyad+Alghamdi%2C+and+Huan+Liu%2E+2024%2E+Can+knowledge+graphs+reduce+hallucinations+in+llms%3F+%3A+A+survey%2E+In+Proceedings+of+the+2024+Conference+of+the+North+American+Chapt)
3. Rami Aly, Zhijiang Guo, Michael Schlichtkrull, James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Oana Cocarascu, and Arpit Mittal. 2021. Feverous: Fact extraction and verification over unstructured and structured information. arXiv preprint arXiv:2106.05707. — [arXiv:2106.05707](https://arxiv.org/abs/2106.05707) [find](https://scholar.google.com/scholar?q=Rami+Aly%2C+Zhijiang+Guo%2C+Michael+Schlichtkrull%2C+James+Thorne%2C+Andreas+Vlachos%2C+Christos+Christodoulopoulos%2C+Oana+Cocarascu%2C+and+Arpit+Mittal%2E+2021%2E+Feverous%3A+Fact+extraction+and+verification+over+unstr)
4. Reid Andersen, Fan R. K. Chung, and Kevin J. Lang. 2006. Local graph partitioning using pagerank vectors. In 47th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2006, Berkeley, California, USA, October 21-24, 2006, Proceedings, pages 475–486. IEEE Computer Society. [find](https://scholar.google.com/scholar?q=Reid+Andersen%2C+Fan+R%2E+K%2E+Chung%2C+and+Kevin+J%2E+Lang%2E+2006%2E+Local+graph+partitioning+using+pagerank+vectors%2E+In+47th+Annual+IEEE+Symposium+on+Foundations+of+Computer+Science%2C+FOCS+2006%2C+Berkeley%2C+Califor)
5. Anthropic. 2024. Claude 3.5 sonnet. [find](https://scholar.google.com/scholar?q=Anthropic%2E+2024%2E+Claude+3%2E5+sonnet%2E)
6. Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. [Self-rag: Learning to retrieve, generate, and critique through self-reflection](https://md.research.elamur.ai/free/en/papers/asai-2023-self-rag-learning-retrieve-generate/). In The Twelfth International Conference on Learning Representations.
7. Huanhuan Cao, Daxin Jiang, Jian Pei, Qi He, Zhen Liao, Enhong Chen, and Hang Li. 2008. Context-aware query suggestion by mining click-through and session data. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 875–883. [find](https://scholar.google.com/scholar?q=Huanhuan+Cao%2C+Daxin+Jiang%2C+Jian+Pei%2C+Qi+He%2C+Zhen+Liao%2C+Enhong+Chen%2C+and+Hang+Li%2E+2008%2E+Context%2Daware+query+suggestion+by+mining+click%2Dthrough+and+session+data%2E+In+Proceedings+of+the+14th+ACM+SIGKDD+in)
8. Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou, and William Yang Wang. 2019. [Tabfact: A large-scale dataset for table-based fact verification](https://md.research.elamur.ai/free/en/papers/chen-2019-tabfact-large-scale-dataset-table/). arXiv preprint arXiv:1909.02164.
9. Wenhu Chen, Hanwen Zha, Zhiyu Chen, Wenhan Xiong, Hong Wang, and William Wang. 2020. [Hybridqa: A dataset of multi-hop question answering over tabular and textual data](https://md.research.elamur.ai/free/en/papers/chen-2020-hybridqa-dataset-multi-hop-question/). arXiv preprint arXiv:2004.07347.
10. Eunsol Choi, Jennimaria Palomaki, Matthew Lamm, Tom Kwiatkowski, Dipanjan Das, and Michael Collins. 2021. [Decontextualization: Making sentences stand-alone](https://md.research.elamur.ai/free/en/papers/choi-2021-decontextualization-making-sentences-stand-alone/). Transactions of the Association for Computational Linguistics, 9:447–461.
11. Vijay Prakash Dwivedi, Charilaos I. Kanatsoulis, Shenyang Huang, and Jure Leskovec. 2025. Relational deep learning: Challenges, foundations and next-generation architectures. CoRR, abs/2506.16654. [find](https://scholar.google.com/scholar?q=Vijay+Prakash+Dwivedi%2C+Charilaos+I%2E+Kanatsoulis%2C+Shenyang+Huang%2C+and+Jure+Leskovec%2E+2025%2E+Relational+deep+learning%3A+Challenges%2C+foundations+and+next%2Dgeneration+architectures%2E+CoRR%2C+abs%2F2506%2E16654%2E)
12. Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, and Jonathan Larson. 2024a. From local to global: A graph RAG approach to query-focused summarization. CoRR, abs/2404.16130. [find](https://scholar.google.com/scholar?q=Darren+Edge%2C+Ha+Trinh%2C+Newman+Cheng%2C+Joshua+Bradley%2C+Alex+Chao%2C+Apurva+Mody%2C+Steven+Truitt%2C+and+Jonathan+Larson%2E+2024a%2E+From+local+to+global%3A+A+graph+RAG+approach+to+query%2Dfocused+summarization%2E+CoRR%2C)
13. Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, and Jonathan Larson. 2024b. From local to global: A graph rag approach to query-focused summarization. arXiv preprint arXiv:2404.16130. [find](https://scholar.google.com/scholar?q=Darren+Edge%2C+Ha+Trinh%2C+Newman+Cheng%2C+Joshua+Bradley%2C+Alex+Chao%2C+Apurva+Mody%2C+Steven+Truitt%2C+and+Jonathan+Larson%2E+2024b%2E+From+local+to+global%3A+A+graph+rag+approach+to+query%2Dfocused+summarization%2E+arXiv)
14. Matthias Fey, Weihua Hu, Kexin Huang, Jan Eric Lenssen, Rishabh Ranjan, Joshua Robinson, Rex Ying, Jiaxuan You, and Jure Leskovec. 2024. Position: Relational deep learning - graph representation learning on relational databases. In Forty-first International Conference on Machine Learning, ICML 2024,… [find](https://scholar.google.com/scholar?q=Matthias+Fey%2C+Weihua+Hu%2C+Kexin+Huang%2C+Jan+Eric+Lenssen%2C+Rishabh+Ranjan%2C+Joshua+Robinson%2C+Rex+Ying%2C+Jiaxuan+You%2C+and+Jure+Leskovec%2E+2024%2E+Position%3A+Relational+deep+learning+%2D+graph+representation+learn)
15. Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Qianyu Guo, Meng Wang, and Haofen Wang. 2023. [Retrieval-augmented generation for large language models: A survey](https://md.research.elamur.ai/free/en/papers/gao-2023-retrieval-augmented-generation-large-language/). CoRR, abs/2312.10997.
16. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, and 1 others. 2024. [The llama 3 herd of models](https://md.research.elamur.ai/free/en/papers/grattafiori-2024-llama-3-herd-models/). arXiv preprint arXiv:2407.21783.
17. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, and 1 others. 2025. [Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2025-deepseek-r1-incentivizing-reasoning-capability/). arXiv preprint arXiv:2501.12948.
18. Bernal Jimenez Gutierrez, Yiheng Shu, Yu Gu, Michihiro Yasunaga, and Yu Su. 2024. [Hipporag: Neurobiologically inspired long-term memory for large language models](https://md.research.elamur.ai/free/en/papers/gutierrez-2024-hipporag-neurobiologically-inspired-long-term/). In Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024, V…
19. Bernal Jiménez Gutiérrez, Yiheng Shu, Weijian Qi, Sizhe Zhou, and Yu Su. 2025. From RAG to memory: Non-parametric continual learning for large language models. CoRR, abs/2502.14802. [find](https://scholar.google.com/scholar?q=Bernal+Jim%C3%A9nez+Guti%C3%A9rrez%2C+Yiheng+Shu%2C+Weijian+Qi%2C+Sizhe+Zhou%2C+and+Yu+Su%2E+2025%2E+From+RAG+to+memory%3A+Non%2Dparametric+continual+learning+for+large+language+models%2E+CoRR%2C+abs%2F2502%2E14802%2E)
20. Haoyu Han, Yu Wang, Harry Shomer, Kai Guo, Jiayuan Ding, Yongjia Lei, Mahantesh Halappanavar, Ryan A. Rossi, Subhabrata Mukherjee, Xianfeng Tang, Qi He, Zhigang Hua, Bo Long, Tong Zhao, Neil Shah, Amin Javari, Yinglong Xia, and Jiliang Tang. 2025. Retrieval-augmented generation with graphs (graphrag… [find](https://scholar.google.com/scholar?q=Haoyu+Han%2C+Yu+Wang%2C+Harry+Shomer%2C+Kai+Guo%2C+Jiayuan+Ding%2C+Yongjia+Lei%2C+Mahantesh+Halappanavar%2C+Ryan+A%2E+Rossi%2C+Subhabrata+Mukherjee%2C+Xianfeng+Tang%2C+Qi+He%2C+Zhigang+Hua%2C+Bo+Long%2C+Tong+Zhao%2C+Neil+Shah%2C+Ami)
21. Jonathan Herzig, Thomas Müller, Syrine Krichene, and Julian Martin Eisenschlos. 2021. [Open domain question answering over tables via dense retrieval](https://md.research.elamur.ai/free/en/papers/herzig-2021-open-domain-question-answering-over/). arXiv preprint arXiv:2103.12011.
22. Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno, and Julian Martin Eisenschlos. 2020. [Tapas: Weakly supervised table parsing via pre-training](https://md.research.elamur.ai/free/en/papers/herzig-2020-tapas-weakly-supervised-table-parsing/). arXiv preprint arXiv:2004.02349.
23. Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, and 1 others. 2022. [Lora: Low-rank adaptation of large language models](https://md.research.elamur.ai/free/en/papers/hu-2021-lora-low-rank-adaptation-large/). ICLR, 1(2):3.
24. Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, and 1 others. 2024. [Gpt-4o system card](https://md.research.elamur.ai/free/en/papers/openai-2024-gpt-4o-system-card/). arXiv preprint arXiv:2410.21276.
25. Mohit Iyyer, Wen-tau Yih, and Ming-Wei Chang. 2017. [Search-based neural structured learning for sequential question answering](https://md.research.elamur.ai/free/en/papers/iyyer-2017-search-based-neural-structured-learning/). In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1821– 1831.
26. Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. 2021. [Unsupervised dense information retrieval with contrastive learning](https://md.research.elamur.ai/free/en/papers/izacard-2021-unsupervised-dense-information-retrieval-contrastive/). arXiv preprint arXiv:2112.09118.
27. Aaron Jaech, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed El-Kishky, Aiden Low, Alec Helyar, Aleksander Madry, Alex Beutel, Alex Carney, and 1 others. 2024. [Openai o1 system card](https://md.research.elamur.ai/free/en/papers/openai-2024-openai-o1-system-card/). arXiv preprint arXiv:2412.16720.
28. Geon Lee, Fanchen Bu, Tina Eliassi-Rad, and Kijung Shin. 2024. A survey on hypergraph mining: Patterns, tools, and generators. CoRR, abs/2401.08878. — [arXiv:2401.08878](https://arxiv.org/abs/2401.08878) [find](https://scholar.google.com/scholar?q=Geon+Lee%2C+Fanchen+Bu%2C+Tina+Eliassi%2DRad%2C+and+Kijung+Shin%2E+2024%2E+A+survey+on+hypergraph+mining%3A+Patterns%2C+tools%2C+and+generators%2E+CoRR%2C+abs%2F2401%2E08878%2E+%E2%80%94+%5BarXiv%3A2401%2E08878%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2401%2E088)
29. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, and 1 others. 2020. [Retrieval-augmented generation for knowledge-intensive nlp tasks](https://md.research.elamur.ai/free/en/papers/lewis-2020-retrieval-augmented-generation-knowledge-intensive/). Advances in Neural Information Processing Systems, 33:9459–…
30. Mufei Li, Siqi Miao, and Pan Li. 2025. Simple is effective: The roles of graphs and large language models in knowledge-graph-based retrieval-augmented generation. ICLR. [find](https://scholar.google.com/scholar?q=Mufei+Li%2C+Siqi+Miao%2C+and+Pan+Li%2E+2025%2E+Simple+is+effective%3A+The+roles+of+graphs+and+large+language+models+in+knowledge%2Dgraph%2Dbased+retrieval%2Daugmented+generation%2E+ICLR%2E)
31. Peng Li, Yeye He, Dror Yashar, Weiwei Cui, Song Ge, Haidong Zhang, Danielle Rifinski Fainman, Dongmei Zhang, and Surajit Chaudhuri. 2023. Table-gpt: Table-tuned gpt for diverse table tasks. arXiv preprint arXiv:2310.09263. — [arXiv:2310.09263](https://arxiv.org/abs/2310.09263) [find](https://scholar.google.com/scholar?q=Peng+Li%2C+Yeye+He%2C+Dror+Yashar%2C+Weiwei+Cui%2C+Song+Ge%2C+Haidong+Zhang%2C+Danielle+Rifinski+Fainman%2C+Dongmei+Zhang%2C+and+Surajit+Chaudhuri%2E+2023%2E+Table%2Dgpt%3A+Table%2Dtuned+gpt+for+diverse+table+tasks%2E+arXiv+prep)
32. Shuaiqi Liu, Jiannong Cao, Ruosong Yang, and Zhiyuan Wen. 2023. Long text and multi-table summarization: Dataset and method. arXiv preprint arXiv:2302.03815. [find](https://scholar.google.com/scholar?q=Shuaiqi+Liu%2C+Jiannong+Cao%2C+Ruosong+Yang%2C+and+Zhiyuan+Wen%2E+2023%2E+Long+text+and+multi%2Dtable+summarization%3A+Dataset+and+method%2E+arXiv+preprint+arXiv%3A2302%2E03815%2E)
33. Lingrui Mei, Jiayu Yao, Yuyao Ge, Yiwei Wang, Baolong Bi, Yujun Cai, Jiazhi Liu, Mingyu Li, Zhong-Zhi Li, Duzhen Zhang, Chenlin Zhou, Jiayi Mao, Tianze Xia, Jiafeng Guo, and Shenghua Liu. 2025. A survey of context engineering for large language models. CoRR, abs/2507.13334. — [arXiv:2507.13334](https://arxiv.org/abs/2507.13334) [find](https://scholar.google.com/scholar?q=Lingrui+Mei%2C+Jiayu+Yao%2C+Yuyao+Ge%2C+Yiwei+Wang%2C+Baolong+Bi%2C+Yujun+Cai%2C+Jiazhi+Liu%2C+Mingyu+Li%2C+Zhong%2DZhi+Li%2C+Duzhen+Zhang%2C+Chenlin+Zhou%2C+Jiayi+Mao%2C+Tianze+Xia%2C+Jiafeng+Guo%2C+and+Shenghua+Liu%2E+2025%2E+A+surv)
34. Vaishali Pal, Andrew Yates, Evangelos Kanoulas, and Maarten de Rijke. 2023. Multitabqa: Generating tabular answers for multi-table question answering. arXiv preprint arXiv:2305.12820. [find](https://scholar.google.com/scholar?q=Vaishali+Pal%2C+Andrew+Yates%2C+Evangelos+Kanoulas%2C+and+Maarten+de+Rijke%2E+2023%2E+Multitabqa%3A+Generating+tabular+answers+for+multi%2Dtable+question+answering%2E+arXiv+preprint+arXiv%3A2305%2E12820%2E)
35. Panupong Pasupat and Percy Liang. 2015. [Compositional semantic parsing on semi-structured tables](https://md.research.elamur.ai/free/en/papers/pasupat-2015-compositional-semantic-parsing-semi-structured/).
36. Boci Peng, Yun Zhu, Yongchao Liu, Xiaohe Bo, Haizhou Shi, Chuntao Hong, Yan Zhang, and Siliang Tang. 2024. [Graph retrieval-augmented generation: A survey](https://md.research.elamur.ai/free/en/papers/peng-2024-graph-retrieval-augmented-generation-survey/). arXiv preprint arXiv:2408.08921.
37. Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham. 2023. [In-context retrieval-augmented language models](https://md.research.elamur.ai/free/en/papers/ram-2023-context-retrieval-augmented-language-models/). Transactions of the Association for Computational Linguistics, 11:1316–1331.
38. N Reimers. 2019. [Sentence-bert: Sentence embeddings using siamese bert-networks](https://md.research.elamur.ai/free/en/papers/reimers-2019-sentence-bert-sentence-embeddings-siamese/). arXiv preprint arXiv:1908.10084.
39. Polina Rozenshtein and Aristides Gionis. 2016. Temporal pagerank. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part II 16, pages 674–689. Springer. [find](https://scholar.google.com/scholar?q=Polina+Rozenshtein+and+Aristides+Gionis%2E+2016%2E+Temporal+pagerank%2E+In+Machine+Learning+and+Knowledge+Discovery+in+Databases%3A+European+Conference%2C+ECML+PKDD+2016%2C+Riva+del+Garda%2C+Italy%2C+September+19%2D23%2C)
40. Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, Christopher Potts, and Matei Zaharia. 2021. [Colbertv2: Effective and efficient retrieval via lightweight late interaction](https://md.research.elamur.ai/free/en/papers/santhanam-2022-colbertv2-effective-efficient-retrieval-lightweight/). arXiv preprint arXiv:2112.01488.
41. Parth Sarthi, Salman Abdullah, Aditi Tuli, Shubh Khanna, Anna Goldie, and Christopher D. Manning. 2024. [RAPTOR: recursive abstractive processing for tree-organized retrieval](https://md.research.elamur.ai/free/en/papers/sarthi-2024-raptor-recursive-abstractive-processing-tree/). In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenReview.n…
42. Kwangwook Seo, Donguk Kwon, and Dongha Lee. 2025. Mt-raig: Novel benchmark and evaluation framework for retrieval-augmented insight generation over multiple tables. arXiv preprint arXiv:2502.11735. [find](https://scholar.google.com/scholar?q=Kwangwook+Seo%2C+Donguk+Kwon%2C+and+Dongha+Lee%2E+2025%2E+Mt%2Draig%3A+Novel+benchmark+and+evaluation+framework+for+retrieval%2Daugmented+insight+generation+over+multiple+tables%2E+arXiv+preprint+arXiv%3A2502%2E11735%2E)
43. Shamane Siriwardhana, Rivindu Weerasekera, Elliott Wen, Tharindu Kaluarachchi, Rajib Rana, and Suranga Nanayakkara. 2023. [Improving the domain adaptation of retrieval augmented generation (rag) models for open domain question answering](https://md.research.elamur.ai/free/en/papers/siriwardhana-2023-improving-domain-adaptation-retrieval-augmented/). Transactions of the Association for Computational Linguistics,…
44. Yuan Sui, Jiaru Zou, Mengyu Zhou, Xinyi He, Lun Du, Shi Han, and Dongmei Zhang. 2023. Tap4llm: Table provider on sampling, augmenting, and packing semi-structured data for large language model reasoning. arXiv preprint arXiv:2312.09039. [find](https://scholar.google.com/scholar?q=Yuan+Sui%2C+Jiaru+Zou%2C+Mengyu+Zhou%2C+Xinyi+He%2C+Lun+Du%2C+Shi+Han%2C+and+Dongmei+Zhang%2E+2023%2E+Tap4llm%3A+Table+provider+on+sampling%2C+augmenting%2C+and+packing+semi%2Dstructured+data+for+large+language+model+reasoni)
45. Svitlana Vakulenko and Vadim Savenkov. 2017. Tableqa: Question answering on tabular data. arXiv preprint arXiv:1705.06504. — [arXiv:1705.06504](https://arxiv.org/abs/1705.06504) [find](https://scholar.google.com/scholar?q=Svitlana+Vakulenko+and+Vadim+Savenkov%2E+2017%2E+Tableqa%3A+Question+answering+on+tabular+data%2E+arXiv+preprint+arXiv%3A1705%2E06504%2E+%E2%80%94+%5BarXiv%3A1705%2E06504%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F1705%2E06504%29)
46. Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, Xiangru Tang, Tianhang Zhang, Cheng Jiayang, Yunzhi Yao, Wenyang Gao, Xuming Hu, Zehan Qi, Yidong Wang, Linyi Yang, Jindong Wang, Xing Xie, Zheng Zhang, and Yue Zhang. 2023a. [Survey on factuality in large language models: Knowledge, retrieval and domain-specif…](https://md.research.elamur.ai/free/en/papers/wang-2023-survey-factuality-large-language-models/)
47. Fei Wang, Zhewei Xu, Pedro Szekely, and Muhao Chen. 2022a. Robust (controlled) table-to-text generation with structure-aware equivariance learning. arXiv preprint arXiv:2205.03972. [find](https://scholar.google.com/scholar?q=Fei+Wang%2C+Zhewei+Xu%2C+Pedro+Szekely%2C+and+Muhao+Chen%2E+2022a%2E+Robust+%28controlled%29+table%2Dto%2Dtext+generation+with+structure%2Daware+equivariance+learning%2E+arXiv+preprint+arXiv%3A2205%2E03972%2E)
48. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei. 2022b. [Text embeddings by weakly-supervised contrastive pre-training](https://md.research.elamur.ai/free/en/papers/wang-2022-text-embeddings-weakly-supervised-contrastive/). arXiv preprint arXiv:2212.03533.
49. Zhiruo Wang, Jun Araki, Zhengbao Jiang, Md Rizwan Parvez, and Graham Neubig. 2023b. [Learning to filter context for retrieval-augmented generation](https://md.research.elamur.ai/free/en/papers/wang-2023-learning-filter-context-retrieval-augmented/). arXiv preprint arXiv:2311.08377.
50. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, and 1 others. 2022. [Chain-of-thought prompting elicits reasoning in large language models](https://md.research.elamur.ai/free/en/papers/wei-2022-chain-thought-prompting-elicits-reasoning/). Advances in neural information processing systems, 35:24824– 24837.
51. Jian Wu, Linyi Yang, Dongyuan Li, Yuliang Ji, Manabu Okumura, and Yue Zhang. Mmqa: Evaluating llms with multi-table multi-hop complex questions. In The Thirteenth International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Jian+Wu%2C+Linyi+Yang%2C+Dongyuan+Li%2C+Yuliang+Ji%2C+Manabu+Okumura%2C+and+Yue+Zhang%2E+Mmqa%3A+Evaluating+llms+with+multi%2Dtable+multi%2Dhop+complex+questions%2E+In+The+Thirteenth+International+Conference+on+Learning+)
52. Fangyuan Xu, Weijia Shi, and Eunsol Choi. 2023. [Recomp: Improving retrieval-augmented lms with compression and selective augmentation](https://md.research.elamur.ai/free/en/papers/xu-2023-recomp-improving-retrieval-augmented-lms/). arXiv preprint arXiv:2310.04408.
53. An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, and 1 others. 2024. Qwen2. 5 technical report. arXiv preprint arXiv:2412.15115. [find](https://scholar.google.com/scholar?q=An+Yang%2C+Baosong+Yang%2C+Beichen+Zhang%2C+Binyuan+Hui%2C+Bo+Zheng%2C+Bowen+Yu%2C+Chengyuan+Li%2C+Dayiheng+Liu%2C+Fei+Huang%2C+Haoran+Wei%2C+and+1+others%2E+2024%2E+Qwen2%2E+5+technical+report%2E+arXiv+preprint+arXiv%3A2412%2E15115)
54. Edward Yeo, Yuxuan Tong, Morry Niu, Graham Neubig, and Xiang Yue. 2025. [Demystifying long chain-of-thought reasoning in llms](https://md.research.elamur.ai/free/en/papers/yeo-2025-demystifying-long-chain-thought-reasoning/). arXiv preprint arXiv:2502.03373.
55. Pengcheng Yin, Graham Neubig, Wen-tau Yih, and Sebastian Riedel. 2020. [Tabert: Pretraining for joint understanding of textual and tabular data](https://md.research.elamur.ai/free/en/papers/yin-2020-tabert-pretraining-joint-understanding-textual/). arXiv preprint arXiv:2005.08314.
56. Robert Van Rooij. 2011. Vagueness and linguistics. In Vagueness: A guide, pages 123–170. Springer. [find](https://scholar.google.com/scholar?q=Robert+Van+Rooij%2E+2011%2E+Vagueness+and+linguistics%2E+In+Vagueness%3A+A+guide%2C+pages+123%E2%80%93170%2E+Springer%2E)
57. Minji Yoon, Woojeong Jin, and U Kang. 2018. Fast and accurate random walk with restart on dynamic graphs with guarantees. In Proceedings of the 2018 World Wide Web Conference, pages 409–418. [find](https://scholar.google.com/scholar?q=Minji+Yoon%2C+Woojeong+Jin%2C+and+U+Kang%2E+2018%2E+Fast+and+accurate+random+walk+with+restart+on+dynamic+graphs+with+guarantees%2E+In+Proceedings+of+the+2018+World+Wide+Web+Conference%2C+pages+409%E2%80%93418%2E)
58. Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. [Graph attention networks](https://md.research.elamur.ai/free/en/papers/veli-vckovic-2017-graph-attention-networks/). arXiv preprint arXiv:1710.10903.
59. Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, and 1 others. 2018. [Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task](https://md.research.elamur.ai/free/en/papers/yu-2018-spider-large-scale-human-labeled/). arXiv preprint arXiv:1809.08887.
60. Xiaohan Yu, Pu Jian, and Chong Chen. 2025. Tablerag: A retrieval augmented generation framework for heterogeneous document reasoning. CoRR, abs/2506.10380. [find](https://scholar.google.com/scholar?q=Xiaohan+Yu%2C+Pu+Jian%2C+and+Chong+Chen%2E+2025%2E+Tablerag%3A+A+retrieval+augmented+generation+framework+for+heterogeneous+document+reasoning%2E+CoRR%2C+abs%2F2506%2E10380%2E)
61. Tianshu Zhang, Xiang Yue, Yifei Li, and Huan Sun. 2023a. Tablellama: Towards open large generalist models for tables. arXiv preprint arXiv:2311.09206. [find](https://scholar.google.com/scholar?q=Tianshu+Zhang%2C+Xiang+Yue%2C+Yifei+Li%2C+and+Huan+Sun%2E+2023a%2E+Tablellama%3A+Towards+open+large+generalist+models+for+tables%2E+arXiv+preprint+arXiv%3A2311%2E09206%2E)
62. Zhebin Zhang, Xinyu Zhang, Yuanhang Ren, Saijiang Shi, Meng Han, Yongkang Wu, Ruofei Lai, and Zhao Cao. 2023b. Iag: Induction-augmented generation framework for answering reasoning questions. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 1– 14. [find](https://scholar.google.com/scholar?q=Zhebin+Zhang%2C+Xinyu+Zhang%2C+Yuanhang+Ren%2C+Saijiang+Shi%2C+Meng+Han%2C+Yongkang+Wu%2C+Ruofei+Lai%2C+and+Zhao+Cao%2E+2023b%2E+Iag%3A+Induction%2Daugmented+generation+framework+for+answering+reasoning+questions%2E+In+Proce)
63. Yilun Zhao, Yunxiang Li, Chenying Li, and Rui Zhang. 2022. [Multihiertt: Numerical reasoning over multi hierarchical tabular and textual data](https://md.research.elamur.ai/free/en/papers/zhao-2022-multihiertt-numerical-reasoning-over-multi/). arXiv preprint arXiv:2206.01347.
64. Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. 2020. Graph neural networks: A review of methods and applications. AI open, 1:57–81. [find](https://scholar.google.com/scholar?q=Jie+Zhou%2C+Ganqu+Cui%2C+Shengding+Hu%2C+Zhengyan+Zhang%2C+Cheng+Yang%2C+Zhiyuan+Liu%2C+Lifeng+Wang%2C+Changcheng+Li%2C+and+Maosong+Sun%2E+2020%2E+Graph+neural+networks%3A+A+review+of+methods+and+applications%2E+AI+open%2C+1%3A5)
65. Jiaru Zou, Soumya Roy, Vinay Kumar Verma, Ziyi Wang, David Wipf, Pan Lu, Sumit Negi, James Zou, and Jingrui He. 2026. TaTToo: Tool-grounded thinking PRM for test-time scaling in tabular reasoning. In The Fourteenth International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Jiaru+Zou%2C+Soumya+Roy%2C+Vinay+Kumar+Verma%2C+Ziyi+Wang%2C+David+Wipf%2C+Pan+Lu%2C+Sumit+Negi%2C+James+Zou%2C+and+Jingrui+He%2E+2026%2E+TaTToo%3A+Tool%2Dgrounded+thinking+PRM+for+test%2Dtime+scaling+in+tabular+reasoning%2E+In+)
66. Table of Contents Prompt Template Table of Contents A Additional Details on T-RAG A.1 Representative Score Interpretation A.2 Graph-aware Context Organization 13 13 13 B MultiTableQA Details B.1 Preliminary Analysis . . . . . . . B.2 Task Type Definition . . . . . . . 14 14 14 C Experiment Setup C.1… [find](https://scholar.google.com/scholar?q=Table+of+Contents+Prompt+Template+Table+of+Contents+A+Additional+Details+on+T%2DRAG+A%2E1+Representative+Score+Interpretation+A%2E2+Graph%2Daware+Context+Organization+13+13+13+B+MultiTableQA+Details+B%2E1+Preli)

*Updated 31 October 2026*