Fetching the paper…
Reading the bibliography…
Table understanding (TU) has achieved promising advancements, but it faces the challenges of the scarcity of manually labeled tables and the presence of complex table structures.To address these challenges, we propose HGT, a framework with a heterogeneous graph (HG)-enhanced large language model (LLM) to tackle few-shot TU tasks.It leverages the LLM by aligning the table semantics with the LLM's parametric knowledge through soft prompts and instruction turning and deals with complex tables by a multi-task pre-training scheme involving three novel multi-granularity self-supervised HG pre-training objectives.We empirically demonstrate the effectiveness of HGT, showing that it outperforms the SOTA for few-shot complex TU on several benchmarks.
TaPas: Weakly Supervised Table Parsing via Pre-training
Herzig, J.; Nowak, P. K.; Müller, T.; Piccinno, F.; and Eisenschlos, J. M. 2020 · 2004
Earlier work this paper cites.
TaPas: Weakly Supervised Table Parsing via Pre-training
Herzig, J.; Nowak, P. K.; Müller, T.; Piccinno, F.; and Eisenschlos, J. M. 2020 · 2004
Earlier work this paper cites.
TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data
Yin, P.; Neubig, G.; tau Yih, W.; and Riedel, S. 2020 · 2005
Earlier work this paper cites.
TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data
Yin, P.; Neubig, G.; tau Yih, W.; and Riedel, S. 2020 · 2005
Earlier work this paper cites.
Compositional Semantic Parsing on Semi-Structured Tables
Pasupat, P.; and Liang, P. 2015 · 2015
Earlier work this paper cites.
Compositional Semantic Parsing on Semi-Structured Tables
Pasupat, P.; and Liang, P. 2015 · 2015
Earlier work this paper cites.
TabVec: Table Vectors for Classification of Web Tables
Ghasemi-Gol, M.; and Szekely, P. A. 2018 · 2018
Earlier work this paper cites.
TabVec: Table Vectors for Classification of Web Tables
Ghasemi-Gol, M.; and Szekely, P. A. 2018 · 2018
Earlier work this paper cites.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019 · 2019
Earlier work this paper cites.
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Reimers, N.; and Gurevych, I. 2019 · 2019
Earlier work this paper cites.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019 · 2019
Earlier work this paper cites.
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Reimers, N.; and Gurevych, I. 2019 · 2019
Earlier work this paper cites.
TabFact: A Large-scale Dataset for Table-based Fact Verification
Chen, W.; Wang, H.; Chen, J.; Zhang, Y.; Wang, H.; Li, S.; Zhou, X.; and Wang, W. Y. 2020 · 2020
Earlier work this paper cites.
TURL: Table Understanding through Representation Learning
Deng, X.; Sun, H.; Lees, A.; Wu, Y.; and Yu, C. 2020 · 2020
Earlier work this paper cites.
Heterogeneous graph transformer
Hu, Z.; Dong, Y.; Wang, K.; and Sun, Y. 2020 · 2020
Earlier work this paper cites.
TabFact: A Large-scale Dataset for Table-based Fact Verification
Chen, W.; Wang, H.; Chen, J.; Zhang, Y.; Wang, H.; Li, S.; Zhou, X.; and Wang, W. Y. 2020 · 2020
Earlier work this paper cites.
TURL: Table Understanding through Representation Learning
Deng, X.; Sun, H.; Lees, A.; Wu, Y.; and Yu, C. 2020 · 2020
Earlier work this paper cites.
Heterogeneous graph transformer
Hu, Z.; Dong, Y.; Wang, K.; and Sun, Y. 2020 · 2020
Earlier work this paper cites.
TabularNet: A Neural Network Architecture for Understanding Semantic Structures of Tabular Data
Du, L.; Gao, F.; Chen, X.; Jia, R.; Wang, J.; Han, S.; and Zhang, D. 2021 · 2021
Earlier work this paper cites.
Lora: Low-rank adaptation of large language models
Hu, E. J.; Shen, Y.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; and Chen, W. 2021 · 2021
Earlier work this paper cites.
Prefix-Tuning: Optimizing Continuous Prompts for Generation
Li, X. L.; and Liang, P. 2021 · 2021
Earlier work this paper cites.
Relative and absolute location embedding for few-shot node classification on graph
Liu, Z.; Fang, Y.; Liu, C.; and Hoi, S. C. 2021 · 2021
Earlier work this paper cites.
Retrieving complex tables with multi-granular graph representation learning
Wang, F.; Sun, K.; Chen, M.; Pujara, J.; and Szekely, P. 2021 · 2021
Earlier work this paper cites.
TabularNet: A Neural Network Architecture for Understanding Semantic Structures of Tabular Data
Du, L.; Gao, F.; Chen, X.; Jia, R.; Wang, J.; Han, S.; and Zhang, D. 2021 · 2021
Earlier work this paper cites.
Lora: Low-rank adaptation of large language models
Hu, E. J.; Shen, Y.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; and Chen, W. 2021 · 2021
Cited alongside, same era.
Prefix-Tuning: Optimizing Continuous Prompts for Generation
Li, X. L.; and Liang, P. 2021 · 2021
Cited alongside, same era.
Relative and absolute location embedding for few-shot node classification on graph
Liu, Z.; Fang, Y.; Liu, C.; and Hoi, S. C. 2021 · 2021
Cited alongside, same era.
Retrieving complex tables with multi-granular graph representation learning
Wang, F.; Sun, K.; Chen, M.; Pujara, J.; and Szekely, P. 2021 · 2021
Cited alongside, same era.
Large Language Models are few(1)-shot Table Reasoners
Chen, W. 2022 · 2022
Cited alongside, same era.
Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality
Chiang, W.-L.; Li, Z.; Lin, Z.; Sheng, Y.; Wu, Z.; Zhang, H.; Zheng, L.; Zhuang, S.; Zhuang, Y.; Gonzalez, J. E.; et al. 2023 · 2023
Later among the works it cites.
GetPt: Graph-enhanced General Table Pre-training with Alternate Attention Network
Jia, R.; Guo, H.; Jin, X.; Yan, C.; Du, L.; Ma, X.; Stankovic, T.; Lozajic, M.; Zoranovic, G.; Ilic, I.; Han, S.; and Zhang, D. 2023 · 2023
Later among the works it cites.
Liu, H.; Li, C.; Wu, Q.; and Lee, Y. J. 2023 · 2023
Later among the works it cites.
Self-supervised learning for heterogeneous graph via structure information based on metapath
Ma, S.; Liu, J.-w.; and Zuo, X. 2023 · 2023
Later among the works it cites.
GraphGPT: Graph Instruction Tuning for Large Language Models
Tang, J.; Yang, Y.; Wei, W.; Shi, L.; Su, L.; Cheng, S.; Yin, D.; and Huang, C. 2023 · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Chen, W.; Ma, X.; Wang, X.; and Cohen, W. W. 2022 · 2022
Cited alongside, same era.
Binding Language Models in Symbolic Languages
Cheng, Z.; Xie, T.; Shi, P.; Li, C.; Nadkarni, R.; Hu, Y.; Xiong, C.; Radev, D. R.; Ostendorf, M.; Zettlemoyer, L.; Smith, N. A.; and Yu, T. 2022 · 2022
Cited alongside, same era.
Table pre-training: A survey on model architectures, pre-training objectives, and downstream tasks
Dong, H.; Cheng, Z.; He, X.; Zhou, M.; Zhou, A.; Zhou, F.; Liu, A.; Han, S.; and Zhang, D. 2022 · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
Ouyang, L.; Wu, J.; Jiang, X.; Almeida, D.; Wainwright, C. L.; Mishkin, P.; Zhang, C.; Agarwal, S.; Slama, K.; Ray, A.; Schulman, J.; Hilton, J.; Kelton, F.; Miller, L. E.; Simens, M.; Askell, A.; Welinder, P.; Christiano, P. F.; Leike, J.; and Lowe, R. J. 2022 · 2022
Cited alongside, same era.
Self-Instruct: Aligning Language Models with Self-Generated Instructions
Wang, Y.; Kordi, Y.; Mishra, S.; Liu, A.; Smith, N. A.; Khashabi, D.; and Hajishirzi, H. 2022 · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Wei, J.; Wang, X.; Schuurmans, D.; Bosma, M.; Xia, F.; Chi, E.; Le, Q. V.; Zhou, D.; et al. 2022 · 2022
Cited alongside, same era.
Self-supervised heterogeneous graph pre-training based on structural clustering
Yang, Y.; Guan, Z.; Wang, Z.; Zhao, W.; Xu, C.; Lu, W.; and Huang, J. 2022 · 2022
Cited alongside, same era.
Later among the works it cites.
Llama 2: Open Foundation and Fine-Tuned Chat Models
Touvron, H.; Martin, L.; Stone, K. R.; Albert, P.; Almahairi, A.; Babaei, Y.; Bashlykov, N.; Batra, S.; Bhargava, P.; Bhosale, S.; Bikel, D. M.; Blecher, L.; Ferrer, C. C.; Chen, M.; Cucurull, G.; Esiobu, D.; Fernandes, J.; Fu, J.; Fu, W.; Fuller, B.; Gao, C.; Goswami, V.; Goyal, N.; Hartshorn, A. S.; Hosseini, S.; Hou, R.; Inan, H.; Kardas, M.; Kerkez, V.; Khabsa, M.; Kloumann, I. M.; Korenev, A. V.; Koura, P. S.; Lachaux, M.-A.; Lavril, T.; Lee, J.; Liskovich, D.; Lu, Y.; Mao, Y.; Martinet, X.; Mihaylov, T.; Mishra, P.; Molybog, I.; Nie, Y.; Poulton, A.; Reizenstein, J.; Rungta, R.; Saladi, K.; Schelten, A.; Silva, R.; Smith, E. M.; Subramanian, R.; Tan, X.; Tang, B.; Taylor, R.; Williams, A.; Kuan, J. X.; Xu, P.; Yan, Z.; Zarov, I.; Zhang, Y.; Fan, A.; Kambadur, M.; Narang, S.; Rodriguez, A.; Stojnic, R.; Edunov, S.; and Scialom, T. 2023 · 2023
Later among the works it cites.
Large language models are versatile decomposers: Decomposing evidence and questions for table-based reasoning
Ye, Y.; Hui, B.; Yang, M.; Li, B.; Huang, F.; and Li, Y. 2023 · 2023
Later among the works it cites.
IM-TQA: A Chinese Table Question Answering Dataset with Implicit and Multi-type Table Structures
Zheng, M.; Hao, Y.; Jiang, W.-J.; Lin, Z.; Lyu, Y.; She, Q.; and Wang, W. 2023 · 2023
Later among the works it cites.
API-Assisted Code Generation for Question Answering on Varied Table Structures
Cao, Y.; Chen, S.; Liu, R.; Wang, Z.; and Fried, D. 2023 · 2023
Later among the works it cites.
Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality
Chiang, W.-L.; Li, Z.; Lin, Z.; Sheng, Y.; Wu, Z.; Zhang, H.; Zheng, L.; Zhuang, S.; Zhuang, Y.; Gonzalez, J. E.; et al. 2023 · 2023
Later among the works it cites.
GetPt: Graph-enhanced General Table Pre-training with Alternate Attention Network
Jia, R.; Guo, H.; Jin, X.; Yan, C.; Du, L.; Ma, X.; Stankovic, T.; Lozajic, M.; Zoranovic, G.; Ilic, I.; Han, S.; and Zhang, D. 2023 · 2023
Later among the works it cites.
Liu, H.; Li, C.; Wu, Q.; and Lee, Y. J. 2023 · 2023
Later among the works it cites.
Self-supervised learning for heterogeneous graph via structure information based on metapath
Ma, S.; Liu, J.-w.; and Zuo, X. 2023 · 2023
Later among the works it cites.
GraphGPT: Graph Instruction Tuning for Large Language Models
Tang, J.; Yang, Y.; Wei, W.; Shi, L.; Su, L.; Cheng, S.; Yin, D.; and Huang, C. 2023 · 2023
Later among the works it cites.
Llama 2: Open Foundation and Fine-Tuned Chat Models
Touvron, H.; Martin, L.; Stone, K. R.; Albert, P.; Almahairi, A.; Babaei, Y.; Bashlykov, N.; Batra, S.; Bhargava, P.; Bhosale, S.; Bikel, D. M.; Blecher, L.; Ferrer, C. C.; Chen, M.; Cucurull, G.; Esiobu, D.; Fernandes, J.; Fu, J.; Fu, W.; Fuller, B.; Gao, C.; Goswami, V.; Goyal, N.; Hartshorn, A. S.; Hosseini, S.; Hou, R.; Inan, H.; Kardas, M.; Kerkez, V.; Khabsa, M.; Kloumann, I. M.; Korenev, A. V.; Koura, P. S.; Lachaux, M.-A.; Lavril, T.; Lee, J.; Liskovich, D.; Lu, Y.; Mao, Y.; Martinet, X.; Mihaylov, T.; Mishra, P.; Molybog, I.; Nie, Y.; Poulton, A.; Reizenstein, J.; Rungta, R.; Saladi, K.; Schelten, A.; Silva, R.; Smith, E. M.; Subramanian, R.; Tan, X.; Tang, B.; Taylor, R.; Williams, A.; Kuan, J. X.; Xu, P.; Yan, Z.; Zarov, I.; Zhang, Y.; Fan, A.; Kambadur, M.; Narang, S.; Rodriguez, A.; Stojnic, R.; Edunov, S.; and Scialom, T. 2023 · 2023
Later among the works it cites.
Large language models are versatile decomposers: Decomposing evidence and questions for table-based reasoning
Ye, Y.; Hui, B.; Yang, M.; Li, B.; Huang, F.; and Li, Y. 2023 · 2023
Later among the works it cites.
IM-TQA: A Chinese Table Question Answering Dataset with Implicit and Multi-type Table Structures
Zheng, M.; Hao, Y.; Jiang, W.-J.; Lin, Z.; Lyu, Y.; She, Q.; and Wang, W. 2023 · 2023
Later among the works it cites.
Large language model for table processing: A survey
Lu, W.; Zhang, J.; Zhang, J.; and Chen, Y. 2024 · 2024
Closest in time.
Table meets llm: Can large language models understand structured table data? a benchmark and empirical study
Sui, Y.; Zhou, M.; Zhou, M.; Han, S.; and Zhang, D. 2024 · 2024
Closest in time.
Chain-of-table: Evolving tables in the reasoning chain for table understanding
Wang, Z.; Zhang, H.; Li, C.-L.; Eisenschlos, J. M.; Perot, V.; Wang, Z.; Miculicich, L.; Fujii, Y.; Shang, J.; Lee, C.-Y.; et al. 2024 · 2024
Closest in time.
Large language model for table processing: A survey
Lu, W.; Zhang, J.; Zhang, J.; and Chen, Y. 2024 · 2024
Closest in time.
Table meets llm: Can large language models understand structured table data? a benchmark and empirical study
Sui, Y.; Zhou, M.; Zhou, M.; Han, S.; and Zhang, D. 2024 · 2024
Closest in time.
Chain-of-table: Evolving tables in the reasoning chain for table understanding
Wang, Z.; Zhang, H.; Li, C.-L.; Eisenschlos, J. M.; Perot, V.; Wang, Z.; Miculicich, L.; Fujii, Y.; Shang, J.; Lee, C.-Y.; et al. 2024 · 2024
Closest in time.