{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T05:17:48Z","timestamp":1784179068909,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":59,"publisher":"ACM","funder":[{"name":"Guangdong Provincial Department of Education Project","award":["2024KQNCX028"],"award-info":[{"award-number":["2024KQNCX028"]}]},{"name":"Hong Kong Research Grants Council under the General Research Fund","award":["15200023"],"award-info":[{"award-number":["15200023"]}]},{"DOI":"10.13039\/501100006374","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072257"],"award-info":[{"award-number":["62072257"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006374","name":"Australian Research Council","doi-asserted-by":"publisher","award":["DP22010371, LE220100078"],"award-info":[{"award-number":["DP22010371, LE220100078"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangzhou-HKUST(GZ) Joint Funding Program","award":["2025A03J3957"],"award-info":[{"award-number":["2025A03J3957"]}]},{"name":"Research Impact Fund","award":["R1015-23"],"award-info":[{"award-number":["R1015-23"]}]},{"name":"CAAI-Ant Group Research Fund","award":["2024312096"],"award-info":[{"award-number":["2024312096"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,7,13]]},"DOI":"10.1145\/3726302.3730002","type":"proceedings-article","created":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T01:25:28Z","timestamp":1752456328000},"page":"1218-1228","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["<i>HyperG:<\/i>\n            Hypergraph-Enhanced LLMs for Structured Knowledge"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1206-2260","authenticated-orcid":false,"given":"Sirui","family":"Huang","sequence":"first","affiliation":[{"name":"University of Technology Sydney, Sydney, Australia and Hong Kong Polytechnic University, Hong Kong SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6332-6241","authenticated-orcid":false,"given":"Hanqian","family":"Li","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4982-8604","authenticated-orcid":false,"given":"Yanggan","family":"Gu","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6075-4224","authenticated-orcid":false,"given":"Xuming","family":"Hu","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3370-471X","authenticated-orcid":false,"given":"Qing","family":"Li","sequence":"additional","affiliation":[{"name":"Hong Kong Polytechnic University, Hong Kong SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4493-6663","authenticated-orcid":false,"given":"Guandong","family":"Xu","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Sydney, Australia and Education University of Hong Kong, Hong Kong SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,7,13]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"2024. Gemma: Open Models Based on Gemini Research and Technology. arXiv:2403.08295 [cs.CL] https:\/\/arxiv.org\/abs\/2403.08295"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107637"},{"key":"e_1_3_2_1_3_1","volume-title":"Graphllm: Boosting graph reasoning ability of large language model. arXiv preprint arXiv:2310.05845","author":"Chai Ziwei","year":"2023","unstructured":"Ziwei Chai, Tianjie Zhang, LiangWu, Kaiqiao Han, Xiaohai Hu, Xuanwen Huang, and Yang Yang. 2023. Graphllm: Boosting graph reasoning ability of large language model. arXiv preprint arXiv:2310.05845 (2023)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.acl-long.705"},{"key":"e_1_3_2_1_5_1","volume-title":"Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al.","author":"Chen Mark","year":"2021","unstructured":"Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde De Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. 2021. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021)."},{"key":"e_1_3_2_1_6_1","volume-title":"HYTREL: Hypergraphenhanced tabular data representation learning. Advances in Neural Information Processing Systems 36","author":"Chen Pei","year":"2024","unstructured":"Pei Chen, Soumajyoti Sarkar, Leonard Lausen, Balasubramaniam Srinivasan, Sheng Zha, Ruihong Huang, and George Karypis. 2024. HYTREL: Hypergraphenhanced tabular data representation learning. Advances in Neural Information Processing Systems 36 (2024)."},{"key":"e_1_3_2_1_7_1","volume-title":"International Conference on Learning Representations.","author":"Chien Eli","unstructured":"Eli Chien, Chao Pan, Jianhao Peng, and Olgica Milenkovic. [n. d.]. You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_8_1","volume-title":"Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI. arXiv preprint arXiv:2407.21523","author":"Cui Lingxi","year":"2024","unstructured":"Lingxi Cui, Huan Li, Ke Chen, Lidan Shou, and Gang Chen. 2024. Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI. arXiv preprint arXiv:2407.21523 (2024)."},{"key":"e_1_3_2_1_9_1","unstructured":"Yunkai Dang Kaichen Huang Jiahao Huo Yibo Yan Sirui Huang Dongrui Liu Mengxi Gao Jie Zhang Chen Qian Kun Wang et al. 2024. Explainable and interpretable multimodal large language models: A comprehensive survey. arXiv preprint arXiv:2412.02104 (2024)."},{"key":"e_1_3_2_1_10_1","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","volume":"1","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 4171--4186."},{"key":"e_1_3_2_1_11_1","volume-title":"Hnhn: Hypergraph networks with hyperedge neurons. arXiv preprint arXiv:2006.12278","author":"Dong Yihe","year":"2020","unstructured":"Yihe Dong, Will Sawin, and Yoshua Bengio. 2020. Hnhn: Hypergraph networks with hyperedge neurons. arXiv preprint arXiv:2006.12278 (2020)."},{"key":"e_1_3_2_1_12_1","volume-title":"Jiani Zhang, Ziqing Hu, Yanjun Jane Qi, Scott Nickleach, Diego Socolinsky, Srinivasan Sengamedu, Christos Faloutsos, et al.","author":"Fang Xi","year":"2024","unstructured":"Xi Fang, Weijie Xu, Fiona Anting Tan, Jiani Zhang, Ziqing Hu, Yanjun Jane Qi, Scott Nickleach, Diego Socolinsky, Srinivasan Sengamedu, Christos Faloutsos, et al. 2024. Large language models (LLMs) on tabular data: Prediction, generation, and understanding-a survey. (2024)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013558"},{"key":"e_1_3_2_1_14_1","volume-title":"Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning. In Forty-first International Conference on Machine Learning.","author":"Han Sungwon","unstructured":"Sungwon Han, Jinsung Yoon, Sercan O Arik, and Tomas Pfister. [n. d.]. Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning. In Forty-first International Conference on Machine Learning."},{"key":"e_1_3_2_1_15_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 5549--5581","author":"Hegselmann Stefan","year":"2023","unstructured":"Stefan Hegselmann, Alejandro Buendia, Hunter Lang, Monica Agrawal, Xiaoyi Jiang, and David Sontag. 2023. Tabllm: Few-shot classification of tabular data with large language models. In International Conference on Artificial Intelligence and Statistics. PMLR, 5549--5581."},{"key":"e_1_3_2_1_16_1","volume-title":"LoRA: Low-Rank Adaptation of Large Language Models. In International Conference on Learning Representations. https: \/\/openreview.net\/forum?id=nZeVKeeFYf9","author":"Hu Edward J","year":"2022","unstructured":"Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022. LoRA: Low-Rank Adaptation of Large Language Models. In International Conference on Learning Representations. https: \/\/openreview.net\/forum?id=nZeVKeeFYf9"},{"key":"e_1_3_2_1_17_1","volume-title":"Towards Understanding Factual Knowledge of Large Language Models. In The Twelfth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=9OevMUdods","author":"Hu Xuming","year":"2024","unstructured":"Xuming Hu, Junzhe Chen, Xiaochuan Li, Yufei Guo, Lijie Wen, Philip S. Yu, and Zhijiang Guo. 2024. Towards Understanding Factual Knowledge of Large Language Models. In The Twelfth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=9OevMUdods"},{"key":"e_1_3_2_1_18_1","volume-title":"Reasoning Factual Knowledge in Structured Data with Large Language Models. arXiv preprint arXiv:2408.12188","author":"Huang Sirui","year":"2024","unstructured":"Sirui Huang, Yanggan Gu, Xuming Hu, Zhonghao Li, Qing Li, and Guandong Xu. 2024. Reasoning Factual Knowledge in Structured Data with Large Language Models. arXiv preprint arXiv:2408.12188 (2024)."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645627"},{"key":"e_1_3_2_1_20_1","volume-title":"Mmunlearner: Reformulating multimodal machine unlearning in the era of multimodal large language models. arXiv preprint arXiv:2502.11051","author":"Huo Jiahao","year":"2025","unstructured":"Jiahao Huo, Yibo Yan, Xu Zheng, Yuanhuiyi Lyu, Xin Zou, Zhihua Wei, and Xuming Hu. 2025. Mmunlearner: Reformulating multimodal machine unlearning in the era of multimodal large language models. arXiv preprint arXiv:2502.11051 (2025)."},{"key":"e_1_3_2_1_21_1","volume-title":"Towards Better Serialization of Tabular Data for Few-shot Classification. arXiv preprint arXiv:2312.12464","author":"Jaitly Sukriti","year":"2023","unstructured":"Sukriti Jaitly, Tanay Shah, Ashish Shugani, and Razik Singh Grewal. 2023. Towards Better Serialization of Tabular Data for Few-shot Classification. arXiv preprint arXiv:2312.12464 (2023)."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.574"},{"key":"e_1_3_2_1_23_1","volume-title":"HGT: Leveraging Heterogeneous Graphenhanced Large Language Models for Few-shot Complex Table Understanding. arXiv preprint arXiv:2403.19723","author":"Jin Rihui","year":"2024","unstructured":"Rihui Jin, Yu Li, Guilin Qi, Nan Hu, Yuan-Fang Li, Jiaoyan Chen, Jianan Wang, Yongrui Chen, and Dehai Min. 2024. HGT: Leveraging Heterogeneous Graphenhanced Large Language Models for Few-shot Complex Table Understanding. arXiv preprint arXiv:2403.19723 (2024)."},{"key":"e_1_3_2_1_24_1","volume-title":"The Twelfth International Conference on Learning Representations.","author":"Kong Kezhi","unstructured":"Kezhi Kong, Jiani Zhang, Zhengyuan Shen, Balasubramaniam Srinivasan, Chuan Lei, Christos Faloutsos, Huzefa Rangwala, and George Karypis. [n. d.]. OpenTab: Advancing Large Language Models as Open-domain Table Reasoners. In The Twelfth International Conference on Learning Representations."},{"key":"e_1_3_2_1_25_1","volume-title":"Proceedings of the 37th International Conference on Neural Information Processing Systems. 4952--4984","author":"Li Hongxin","year":"2023","unstructured":"Hongxin Li, Jingran Su, Yuntao Chen, Qing Li, and Zhaoxiang Zhang. 2023. SheetCopilot: bringing software productivity to the next level through large language models. In Proceedings of the 37th International Conference on Neural Information Processing Systems. 4952--4984."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3481900"},{"key":"e_1_3_2_1_27_1","volume-title":"HOT-GAN: Hilbert Optimal Transport for Generative Adversarial Network","author":"Li Qian","year":"2024","unstructured":"Qian Li, Zhichao Wang, Haiyang Xia, Gang Li, Yanan Cao, Lina Yao, and Guandong Xu. 2024. HOT-GAN: Hilbert Optimal Transport for Generative Adversarial Network. IEEE Transactions on Neural Networks and Learning Systems (2024)."},{"key":"e_1_3_2_1_28_1","volume-title":"Snapkv: Llm knows what you are looking for before generation. arXiv preprint arXiv:2404.14469","author":"Li Yuhong","year":"2024","unstructured":"Yuhong Li, Yingbing Huang, Bowen Yang, Bharat Venkitesh, Acyr Locatelli, Hanchen Ye, Tianle Cai, Patrick Lewis, and Deming Chen. 2024. Snapkv: Llm knows what you are looking for before generation. arXiv preprint arXiv:2404.14469 (2024)."},{"key":"e_1_3_2_1_29_1","volume-title":"Refiner: Restructure retrieval content efficiently to advance question answering capabilities. arXiv preprint arXiv:2406.11357","author":"Li Zhonghao","year":"2024","unstructured":"Zhonghao Li, Xuming Hu, Aiwei Liu, Kening Zheng, Sirui Huang, and Hui Xiong. 2024. Refiner: Restructure retrieval content efficiently to advance question answering capabilities. arXiv preprint arXiv:2406.11357 (2024)."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00638"},{"key":"e_1_3_2_1_31_1","volume-title":"Git-mol: A multimodal large language model for molecular science with graph, image, and text. Computers in biology and medicine 171","author":"Liu Pengfei","year":"2024","unstructured":"Pengfei Liu, Yiming Ren, Jun Tao, and Zhixiang Ren. 2024. Git-mol: A multimodal large language model for molecular science with graph, image, and text. Computers in biology and medicine 171 (2024), 108073."},{"key":"e_1_3_2_1_32_1","volume-title":"Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models. In Thirty-seventh Conference on Neural Information Processing Systems. https:\/\/openreview.net\/forum?id=HtqnVSCj3q","author":"Lu Pan","year":"2023","unstructured":"Pan Lu, Baolin Peng, Hao Cheng, Michel Galley, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, and Jianfeng Gao. 2023. Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models. In Thirty-seventh Conference on Neural Information Processing Systems. https:\/\/openreview.net\/forum?id=HtqnVSCj3q"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"crossref","unstructured":"Dehai Min Nan Hu Rihui Jin Nuo Lin Jiaoyan Chen Yongrui Chen Yu Li Guilin Qi Yun Li Nijun Li et al. 2024. Exploring the impact of table-to-text methods on augmenting llm-based question answering with domain hybrid data. arXiv preprint arXiv:2402.12869 (2024).","DOI":"10.18653\/v1\/2024.naacl-industry.41"},{"key":"e_1_3_2_1_34_1","volume-title":"https:\/\/platform.openai.com\/docs\/models\/gpt-3-5-turbo Accessed","author":"Turbo AI.","year":"2024","unstructured":"OpenAI. 2023. OpenAI's GPT-3.5 Turbo. https:\/\/platform.openai.com\/docs\/models\/gpt-3-5-turbo Accessed: October 6, 2024."},{"key":"e_1_3_2_1_35_1","unstructured":"OpenAI. 2024. GPT-4o mini: advancing cost-efficient intelligence. https:\/\/openai.com\/index\/gpt-4o-mini-advancing-cost-efficient-intelligence"},{"key":"e_1_3_2_1_36_1","volume-title":"Compositional semantic parsing on semi-structured tables. arXiv preprint arXiv:1508.00305","author":"Pasupat Panupong","year":"2015","unstructured":"Panupong Pasupat and Percy Liang. 2015. Compositional semantic parsing on semi-structured tables. arXiv preprint arXiv:1508.00305 (2015)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-1142"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-naacl.130"},{"key":"e_1_3_2_1_39_1","volume-title":"Evaluating the text-to-sql capabilities of large language models. arXiv preprint arXiv:2204.00498","author":"Rajkumar Nitarshan","year":"2022","unstructured":"Nitarshan Rajkumar, Raymond Li, and Dzmitry Bahdanau. 2022. Evaluating the text-to-sql capabilities of large language models. arXiv preprint arXiv:2204.00498 (2022)."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671460"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.naacl-long.8"},{"key":"e_1_3_2_1_42_1","volume-title":"Impacts on Table Structure Understanding Tasks in LLMs. In NeurIPS 2023 Second Table Representation Learning Workshop.","author":"Singha Ananya","unstructured":"Ananya Singha, Jos\u00e9 Cambronero, Sumit Gulwani, Vu Le, and Chris Parnin. [n. d.]. Tabular Representation, Noisy Operators, and Impacts on Table Structure Understanding Tasks in LLMs. In NeurIPS 2023 Second Table Representation Learning Workshop."},{"key":"e_1_3_2_1_43_1","volume-title":"Struct-X: Enhancing Large Language Models Reasoning with Structured Data. arXiv preprint arXiv:2407.12522","author":"Tan Xiaoyu","year":"2024","unstructured":"Xiaoyu Tan, Haoyu Wang, Xihe Qiu, Yuan Cheng, Yinghui Xu, Wei Chu, and Yuan Qi. 2024. Struct-X: Enhancing Large Language Models Reasoning with Structured Data. arXiv preprint arXiv:2407.12522 (2024)."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i17.29875"},{"key":"e_1_3_2_1_45_1","unstructured":"Hugo Touvron et al. 2024. The Llama 3 Herd of Models. arXiv:2407.21783 [cs.CL] https:\/\/arxiv.org\/abs\/2407.21783"},{"key":"e_1_3_2_1_46_1","volume-title":"Attention is all you need. Advances in Neural Information Processing Systems","author":"Vaswani A","year":"2017","unstructured":"A Vaswani. 2017. Attention is all you need. Advances in Neural Information Processing Systems (2017)."},{"key":"e_1_3_2_1_47_1","volume-title":"Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding. In The Twelfth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=4L0xnS4GQM","author":"Wang Zilong","year":"2024","unstructured":"Zilong Wang, Hao Zhang, Chun-Liang Li, Julian Martin Eisenschlos, Vincent Perot, Zifeng Wang, Lesly Miculicich, Yasuhisa Fujii, Jingbo Shang, Chen-Yu Lee, and Tomas Pfister. 2024. Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding. In The Twelfth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=4L0xnS4GQM"},{"key":"e_1_3_2_1_48_1","volume-title":"International Conference on Learning Representations (ICLR). Addis Ababa, Ethiopia.","author":"Yunkai Zhang Hong Wang Jianshu Chen","year":"2020","unstructured":"Jianshu Chen Yunkai Zhang Hong Wang Shiyang Li Xiyou Zhou Wenhu Chen, Hongmin Wang and William Yang Wang. 2020. TabFact : A Large-scale Dataset for Table-based Fact Verification. In International Conference on Learning Representations (ICLR). Addis Ababa, Ethiopia."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3654992"},{"key":"e_1_3_2_1_50_1","volume-title":"Hypergcn: A new method for training graph convolutional networks on hypergraphs. Advances in neural information processing systems 32","author":"Yadati Naganand","year":"2019","unstructured":"Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, and Partha Talukdar. 2019. Hypergcn: A new method for training graph convolutional networks on hypergraphs. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_3_2_1_51_1","volume-title":"Latexgcl: Large language models (llms)-based data augmentation for text-attributed graph contrastive learning. arXiv preprint arXiv:2409.01145","author":"Yang Haoran","year":"2024","unstructured":"Haoran Yang, Xiangyu Zhao, Sirui Huang, Qing Li, and Guandong Xu. 2024. Latexgcl: Large language models (llms)-based data augmentation for text-attributed graph contrastive learning. arXiv preprint arXiv:2409.01145 (2024)."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591708"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591708"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591708"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.naacl-long.335"},{"key":"e_1_3_2_1_56_1","volume-title":"Thirty-seventh Conference on Neural Information Processing Systems. https:\/\/openreview.net\/forum?id=RkRrPp7GKO","author":"Zhang Zhenyu","year":"2023","unstructured":"Zhenyu Zhang, Ying Sheng, Tianyi Zhou, Tianlong Chen, Lianmin Zheng, Ruisi Cai, Zhao Song, Yuandong Tian, Christopher Re, Clark Barrett, Zhangyang Wang, and Beidi Chen. 2023. H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models. In Thirty-seventh Conference on Neural Information Processing Systems. https:\/\/openreview.net\/forum?id=RkRrPp7GKO"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-industry.17"},{"key":"e_1_3_2_1_58_1","volume-title":"Large Language Models Are Not Robust Multiple Choice Selectors. In The Twelfth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=shr9PXz7T0","author":"Zheng Chujie","year":"2024","unstructured":"Chujie Zheng, Hao Zhou, Fandong Meng, Jie Zhou, and Minlie Huang. 2024. Large Language Models Are Not Robust Multiple Choice Selectors. In The Twelfth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=shr9PXz7T0"},{"key":"e_1_3_2_1_59_1","volume-title":"NeurIPS 2023 Second Table Representation Learning Workshop.","author":"Zhu Max","unstructured":"Max Zhu, Sini\u0161a Stanivuk, Andrija Petrovic, Mladen Nikolic, and Pietro Lio. [n. d.]. Incorporating LLM Priors into Tabular Learners. In NeurIPS 2023 Second Table Representation Learning Workshop."}],"event":{"name":"SIGIR '25: The 48th International ACM SIGIR Conference on Research and Development in Information Retrieval","location":"Padua Italy","acronym":"SIGIR '25","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3726302.3730002","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T12:19:53Z","timestamp":1755865193000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3726302.3730002"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,13]]},"references-count":59,"alternative-id":["10.1145\/3726302.3730002","10.1145\/3726302"],"URL":"https:\/\/doi.org\/10.1145\/3726302.3730002","relation":{},"subject":[],"published":{"date-parts":[[2025,7,13]]},"assertion":[{"value":"2025-07-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}