{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T15:14:19Z","timestamp":1776784459917,"version":"3.51.2"},"reference-count":99,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T00:00:00Z","timestamp":1737504000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T00:00:00Z","timestamp":1737504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Front. Comput. Sci."],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s11704-024-40330-z","type":"journal-article","created":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T15:31:22Z","timestamp":1737559882000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A survey of table reasoning with large language models"],"prefix":"10.1007","volume":"19","author":[{"given":"Xuanliang","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dingzirui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Longxu","family":"Dou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingfu","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wanxiang","family":"Che","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,22]]},"reference":[{"key":"40330_CR1","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1007\/978-981-19-7596-7_14","volume-title":"Proceedings of the 7th China Conference on Knowledge Graph and Semantic Computing: Knowledge Graph Empowers the Digital Economy","author":"N Jin","year":"2022","unstructured":"Jin N, Siebert J, Li D, Chen Q. A survey on table question answering: recent advances. In: Proceedings of the 7th China Conference on Knowledge Graph and Semantic Computing: Knowledge Graph Empowers the Digital Economy. 2022, 174\u2013186"},{"key":"40330_CR2","first-page":"1470","volume-title":"Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)","author":"P Pasupat","year":"2015","unstructured":"Pasupat P, Liang P. Compositional semantic parsing on semi-structured tables. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015, 1470\u20131480"},{"key":"40330_CR3","volume-title":"Proceedings of the 8th International Conference on Learning Representations","author":"W Chen","year":"2020","unstructured":"Chen W, Wang H, Chen J, Zhang Y, Wang H, Li S, Zhou X, Wang W Y. TabFact: a large-scale dataset for table-based fact verification. In: Proceedings of the 8th International Conference on Learning Representations. 2020"},{"key":"40330_CR4","volume-title":"A survey of large language models","author":"W X Zhao","year":"2023","unstructured":"Zhao W X, Zhou K, Li J, Tang T, Wang X, Hou Y, Min Y, Zhang B, Zhang J, Dong Z, Du Y, Yang C, Chen Y, Chen Z, Jiang J, Ren R, Li Y, Tang X, Liu Z, Liu P, Nie J Y, Wen J R. A survey of large language models. 2023, arXiv preprint arXiv: 2303.18223"},{"key":"40330_CR5","first-page":"6024","volume-title":"Proceedings of 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)","author":"T Zhang","year":"2024","unstructured":"Zhang T, Yue X, Li Y, Sun H. TableLlama: towards open large generalist models for tables. In: Proceedings of 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024, 6024\u20136044"},{"key":"40330_CR6","doi-asserted-by":"publisher","first-page":"5126","DOI":"10.18653\/v1\/2023.emnlp-main.312","volume-title":"Proceedings of 2023 Conference on Empirical Methods in Natural Language Processing","author":"R Zhong","year":"2023","unstructured":"Zhong R, Snell C, Klein D, Eisner J. Non-programmers can label programs indirectly via active examples: a case study with text-to-SQL. In: Proceedings of 2023 Conference on Empirical Methods in Natural Language Processing. 2023, 5126\u20135152"},{"key":"40330_CR7","doi-asserted-by":"publisher","first-page":"542","DOI":"10.18653\/v1\/2023.findings-emnlp.39","volume-title":"Proceedings of the Association for Computational Linguistics: EMNLP 2023","author":"R Sun","year":"2023","unstructured":"Sun R, Arik S, Sinha R, Nakhost H, Dai H, Yin P, Pfister T. SQLPrompt: in-context text-to-SQL with minimal labeled data. In: Proceedings of the Association for Computational Linguistics: EMNLP 2023. 2023, 542\u2013550"},{"key":"40330_CR8","first-page":"26106","volume-title":"Proceedings of the 40th International Conference on Machine Learning","author":"A Ni","year":"2023","unstructured":"Ni A, Iyer S, Radev D, Stoyanov V, Yih W T, Wang S, Lin X V. LEVER: learning to verify language-to-code generation with execution. In: Proceedings of the 40th International Conference on Machine Learning. 2023, 26106\u201326128"},{"key":"40330_CR9","doi-asserted-by":"publisher","first-page":"14174","DOI":"10.18653\/v1\/2023.findings-emnlp.944","volume-title":"Proceedings of the Association for Computational Linguistics: EMNLP 2023","author":"S Chang","year":"2023","unstructured":"Chang S, Fosler-Lussier E. Selective demonstrations for cross-domain text-to-SQL. In: Proceedings of the Association for Computational Linguistics: EMNLP 2023. 2023, 14174\u201314189"},{"issue":"5","key":"40330_CR10","doi-asserted-by":"publisher","first-page":"1132","DOI":"10.14778\/3641204.3641221","volume":"17","author":"D Gao","year":"2024","unstructured":"Gao D, Wang H, Li Y, Sun X, Qian Y, Ding B, Zhou J. Text-to-SQL empowered by large language models: a benchmark evaluation. Proceedings of the VLDB Endowment, 2024, 17(5): 1132\u20131145","journal-title":"Proceedings of the VLDB Endowment"},{"key":"40330_CR11","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1145\/3539618.3591708","volume-title":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Y Ye","year":"2023","unstructured":"Ye Y, Hui B, Yang M, Li B, Huang F, Li Y. Large language models are versatile decomposers: decomposing evidence and questions for table-based reasoning. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2023, 174\u2013184"},{"key":"40330_CR12","volume-title":"Proceedings of the 11th International Conference on Learning Representations","author":"Z Cheng","year":"2023","unstructured":"Cheng Z, Xie T, Shi P, Li C, Nadkarni R, Hu Y, Xiong C, Radev D, Ostendorf M, Zettlemoyer L, Smith N A, Yu T. Binding language models in symbolic languages. In: Proceedings of the 11th International Conference on Learning Representations. 2023"},{"key":"40330_CR13","doi-asserted-by":"publisher","first-page":"11069","DOI":"10.18653\/v1\/2023.findings-acl.704","volume-title":"Proceedings of the Association for Computational Linguistics: ACL 2023","author":"S Saha","year":"2023","unstructured":"Saha S, Yu X, Bansal M, Pasunuru R, Celikyilmaz A. MURMUR: modular multi-step reasoning for semi-structured data-to-text generation. In: Proceedings of the Association for Computational Linguistics: ACL 2023. 2023, 11069\u201311090"},{"key":"40330_CR14","volume-title":"Proceedings of the 12th International Conference on Learning Representations","author":"Z Wang","year":"2024","unstructured":"Wang Z, Zhang H, Li C L, Eisenschlos J M, Perot V, Wang Z, Miculicich L, Fujii Y, Shang J, Lee C Y, Pfister T. Chain-of-table: evolving tables in the reasoning chain for table understanding. In: Proceedings of the 12th International Conference on Learning Representations. 2024"},{"key":"40330_CR15","volume-title":"ChartX & ChartVLM: a versatile benchmark and foundation model for complicated chart reasoning","author":"R Xia","year":"2024","unstructured":"Xia R, Zhang B, Ye H, Yan X, Liu Q, Zhou H, Chen Z, Dou M, Shi B, Yan J, Qiao Y. ChartX & ChartVLM: a versatile benchmark and foundation model for complicated chart reasoning. 2024, arXiv preprint arXiv: 2402.12185"},{"key":"40330_CR16","volume-title":"QDA-SQL: questions enhanced dialogue augmentation for multi-turn text-to-SQL","author":"Y Sun","year":"2024","unstructured":"Sun Y, Guo Z, Yu H, Liu C, Li X, Wang B, Yu X, Zhao T. QDA-SQL: questions enhanced dialogue augmentation for multi-turn text-to-SQL. 2024, arXiv preprint arXiv: 2406.10593"},{"key":"40330_CR17","doi-asserted-by":"publisher","first-page":"10997","DOI":"10.18653\/v1\/2024.findings-acl.653","volume-title":"Proceedings of the Association for Computational Linguistics ACL 2024","author":"Z Hong","year":"2024","unstructured":"Hong Z, Yuan Z, Chen H, Zhang Q, Huang F, Huang X. Knowledge-to-SQL: enhancing SQL generation with data expert LLM. In: Proceedings of the Association for Computational Linguistics ACL 2024. 2024, 10997\u201311008"},{"key":"40330_CR18","first-page":"1","volume-title":"Proceedings of the 4th Workshop on Fact Extraction and VERification (FEVER)","author":"R Aly","year":"2021","unstructured":"Aly R, Guo Z, Schlichtkrull M S, Thorne J, Vlachos A, Christodoulopoulos C, Cocarascu O, Mittal A. The fact extraction and VERification over unstructured and structured information (FEVEROUS) shared task. In: Proceedings of the 4th Workshop on Fact Extraction and VERification (FEVER). 2021, 1\u201313"},{"key":"40330_CR19","first-page":"2046","volume-title":"Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)","author":"A Wuehrl","year":"2024","unstructured":"Wuehrl A, Menchaca Resendiz Y, Grimminger L, Klinger R. What makes medical claims (UN)verifiable? Analyzing entity and relation properties for fact verification. In: Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). 2024, 2046\u20132058"},{"key":"40330_CR20","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1162\/tacl_a_00454","volume":"10","author":"Z Guo","year":"2022","unstructured":"Guo Z, Schlichtkrull M, Vlachos A. A survey on automated fact-checking. Transactions of the Association for Computational Linguistics, 2022, 10: 178\u2013206","journal-title":"Transactions of the Association for Computational Linguistics"},{"key":"40330_CR21","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1162\/tacl_a_00446","volume":"10","author":"L Nan","year":"2022","unstructured":"Nan L, Hsieh C, Mao Z, Lin X V, Verma N, Zhang R, Kry\u015bci\u0144ski W, Schoelkopf H, Kong R, Tang X, Mutuma M, Rosand B, Trindade I, Bandaru R, Cunningham J, Xiong C, Radev D, Radev D. FeTaQA: freeform table question answering. Transactions of the Association for Computational Linguistics, 2022, 10: 35\u201349","journal-title":"Transactions of the Association for Computational Linguistics"},{"key":"40330_CR22","doi-asserted-by":"publisher","first-page":"3911","DOI":"10.18653\/v1\/D18-1425","volume-title":"Proceedings of 2018 Conference on Empirical Methods in Natural Language Processing","author":"T Yu","year":"2018","unstructured":"Yu T, Zhang R, Yang K, Yasunaga M, Wang D, Li Z, Ma J, Li I, Yao Q, Roman S, Zhang Z, Radev D. Spider: a large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-SQL task. In: Proceedings of 2018 Conference on Empirical Methods in Natural Language Processing. 2018, 3911\u20133921"},{"key":"40330_CR23","volume-title":"Proceedings of the 11th International Conference on Learning Representations","author":"P Lu","year":"2023","unstructured":"Lu P, Qiu L, Chang K W, Wu Y N, Zhu S C, Rajpurohit T, Clark P, Kalyan A. Dynamic prompt learning via policy gradient for semi-structured mathematical reasoning. In: Proceedings of the 11th International Conference on Learning Representations. 2023"},{"key":"40330_CR24","doi-asserted-by":"publisher","first-page":"1094","DOI":"10.18653\/v1\/2022.acl-long.78","volume-title":"Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","author":"Z Cheng","year":"2022","unstructured":"Cheng Z, Dong H, Wang Z, Jia R, Guo J, Gao Y, Han S, Lou J G, Zhang D. HiTab: a hierarchical table dataset for question answering and natural language generation. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022, 1094\u20131110"},{"key":"40330_CR25","first-page":"305","volume-title":"Proceedings of 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track","author":"Y Katsis","year":"2022","unstructured":"Katsis Y, Chemmengath S, Kumar V, Bharadwaj S, Canim M, Glass M, Gliozzo A, Pan F, Sen J, Sankaranarayanan K, Chakrabarti S. AIT-QA: question answering dataset over complex tables in the airline industry. In: Proceedings of 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track. 2022, 305\u2013314"},{"key":"40330_CR26","doi-asserted-by":"publisher","first-page":"7787","DOI":"10.18653\/v1\/2023.emnlp-main.483","volume-title":"Proceedings of 2023 Conference on Empirical Methods in Natural Language Processing","author":"X Lu","year":"2023","unstructured":"Lu X, Pan L, Liu Q, Nakov P, Kan M Y. SCITAB: a challenging benchmark for compositional reasoning and claim verification on scientific tables. In: Proceedings of 2023 Conference on Empirical Methods in Natural Language Processing. 2023, 7787\u20137813"},{"key":"40330_CR27","doi-asserted-by":"publisher","first-page":"2309","DOI":"10.18653\/v1\/2020.acl-main.210","volume-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","author":"V Gupta","year":"2020","unstructured":"Gupta V, Mehta M, Nokhiz P, Srikumar V. INFOTABS: inference on tables as semi-structured data. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020, 2309\u20132324"},{"key":"40330_CR28","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.18653\/v1\/2020.emnlp-main.89","volume-title":"Proceedings of 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)","author":"A Parikh","year":"2020","unstructured":"Parikh A, Wang X, Gehrmann S, Faruqui M, Dhingra B, Yang D, Das D. ToTTo: a controlled table-to-text generation dataset. In: Proceedings of 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020, 1173\u20131186"},{"key":"40330_CR29","doi-asserted-by":"publisher","first-page":"1157","DOI":"10.18653\/v1\/2023.emnlp-main.74","volume-title":"Proceedings of 2023 Conference on Empirical Methods in Natural Language Processing","author":"Y Zhao","year":"2023","unstructured":"Zhao Y, Qi Z, Nan L, Mi B, Liu Y, Zou W, Han S, Chen R, Tang X, Xu Y, Radev D, Cohan A. QTSumm: query-focused summarization over tabular data. In: Proceedings of 2023 Conference on Empirical Methods in Natural Language Processing. 2023, 1157\u20131172"},{"key":"40330_CR30","volume-title":"Proceedings of the 1st Neural Information Processing Systems Track on Datasets and Benchmarks","author":"N Moosavi","year":"2021","unstructured":"Moosavi N, R\u00fcckl\u00e9 A, Roth D, Gurevych I. SciGen: a dataset for reasoning-aware text generation from scientific tables. In: Proceedings of the 1st Neural Information Processing Systems Track on Datasets and Benchmarks. 2021"},{"key":"40330_CR31","doi-asserted-by":"publisher","first-page":"7929","DOI":"10.18653\/v1\/2020.acl-main.708","volume-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","author":"W Chen","year":"2020","unstructured":"Chen W, Chen J, Su Y, Chen Z, Wang W Y. Logical natural language generation from open-domain tables. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020, 7929\u20137942"},{"key":"40330_CR32","doi-asserted-by":"publisher","first-page":"193","DOI":"10.18653\/v1\/2021.findings-acl.17","volume-title":"Proceedings of the Association for Computational Linguistics: ACL-IJCNLP 2021","author":"M Chen","year":"2021","unstructured":"Chen M, Wiseman S, Gimpel K. WikiTableT: a large-scale data-to-text dataset for generating Wikipedia article sections. In: Proceedings of the Association for Computational Linguistics: ACL-IJCNLP 2021. 2021, 193\u2013209"},{"key":"40330_CR33","volume-title":"Seq2SQL: generating structured queries from natural language using reinforcement learning","author":"V Zhong","year":"2017","unstructured":"Zhong V, Xiong C, Socher R. Seq2SQL: generating structured queries from natural language using reinforcement learning. 2017, arXiv preprint arXiv: 1709.00103"},{"key":"40330_CR34","doi-asserted-by":"publisher","first-page":"1962","DOI":"10.18653\/v1\/D19-1204","volume-title":"Proceedings of 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)","author":"T Yu","year":"2019","unstructured":"Yu T, Zhang R, Er H, Li S, Xue E, Pang B, Lin X V, Tan Y C, Shi T, Li Z, Jiang Y, Yasunaga M, Shim S, Chen T, Fabbri A, Li Z, Chen L, Zhang Y, Dixit S, Zhang V, Xiong C, Socher R, Lasecki W, Radev D. CoSQL: a conversational text-to-SQL challenge towards cross-domain natural language interfaces to databases. In: Proceedings of 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019, 1962\u20131979"},{"key":"40330_CR35","doi-asserted-by":"publisher","first-page":"1849","DOI":"10.18653\/v1\/2020.findings-emnlp.167","volume-title":"Proceedings of the Association for Computational Linguistics: EMNLP 2020","author":"T Shi","year":"2020","unstructured":"Shi T, Zhao C, Boyd-Graber J, Daum\u00e9III H, Lee L. On the potential of Lexico-logical alignments for semantic parsing to SQL queries. In: Proceedings of the Association for Computational Linguistics: EMNLP 2020. 2020, 1849\u20131864"},{"key":"40330_CR36","first-page":"2261","volume-title":"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)","author":"C H Lee","year":"2021","unstructured":"Lee C H, Polozov O, Richardson M. KaggleDBQA: realistic evaluation of text-to-SQL parsers. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021, 2261\u20132273"},{"key":"40330_CR37","first-page":"1835","volume-title":"Proceedings of the 37th International Conference on Neural Information Processing Systems","author":"J Li","year":"2024","unstructured":"Li J, Hui B, Qu G, Yang J, Li B, Li B, Wang B, Qin B, Geng R, Huo N, Zhou X, Ma C, Li G, Chang K C C, Huang F, Cheng R, Li Y. Can LLM already serve as a database interface? A big bench for large-scale database grounded text-to-SQLs. In: Proceedings of the 37th International Conference on Neural Information Processing Systems. 2024, 1835"},{"key":"40330_CR38","doi-asserted-by":"publisher","first-page":"14786","DOI":"10.18653\/v1\/2023.emnlp-main.914","volume-title":"Proceedings of 2023 Conference on Empirical Methods in Natural Language Processing","author":"B Zhao","year":"2023","unstructured":"Zhao B, Ji C, Zhang Y, He W, Wang Y, Wang Q, Feng R, Zhang X. Large language models are complex table parsers. In: Proceedings of 2023 Conference on Empirical Methods in Natural Language Processing. 2023, 14786\u201314802"},{"key":"40330_CR39","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1145\/3616855.3635752","volume-title":"Proceedings of the 17th ACM International Conference on Web Search and Data Mining","author":"Y Sui","year":"2024","unstructured":"Sui Y, Zhou M, Zhou M, Han S, Zhang D. Table meets LLM: can large language models understand structured table data? A benchmark and empirical study. In: Proceedings of the 17th ACM International Conference on Web Search and Data Mining. 2024, 645\u2013654"},{"key":"40330_CR40","volume-title":"Proceedings of NeurIPS 2023 Second Table Representation Learning Workshop","author":"A Singha","year":"2023","unstructured":"Singha A, Cambronero J, Gulwani S, Le V, Parnin C. Tabular representation, noisy operators, and impacts on table structure understanding tasks in LLMs. In: Proceedings of NeurIPS 2023 Second Table Representation Learning Workshop. 2023"},{"key":"40330_CR41","first-page":"13067","volume-title":"Proceedings of the 37th AAAI Conference on Artificial Intelligence","author":"H Li","year":"2023","unstructured":"Li H, Zhang J, Li C, Chen H. RESDSQL: decoupling schema linking and skeleton parsing for text-to-SQL. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence. 2023, 13067\u201313075"},{"key":"40330_CR42","first-page":"1577","volume-title":"Proceedings of the 37th International Conference on Neural Information Processing Systems","author":"M Pourreza","year":"2023","unstructured":"Pourreza M, Rafiei D. DIN-SQL: decomposed in-context learning of text-to-SQL with self-correction. In: Proceedings of the 37th International Conference on Neural Information Processing Systems. 2023, 1577"},{"key":"40330_CR43","volume-title":"DAC: decomposed automation correction for text-to-SQL","author":"D Wang","year":"2024","unstructured":"Wang D, Dou L, Zhang X, Zhu Q, Che W. DAC: decomposed automation correction for text-to-SQL. 2024, arXiv preprint arXiv: 2408.08779"},{"key":"40330_CR44","volume-title":"TableGPT: towards unifying tables, nature language and commands into one GPT","author":"L Zha","year":"2023","unstructured":"Zha L, Zhou J, Li L, Wang R, Huang Q, Yang S, Yuan J, Su C, Li X, Su A, Zhang T, Zhou C, Shou K, Wang M, Zhu W, Lu G, Ye C, Ye Y, Ye W, Zhang Y, Deng X, Xu J, Wang H, Chen G, Zhao J. TableGPT: towards unifying tables, nature language and commands into one GPT. 2023, arXiv preprint arXiv: 2307.08674"},{"key":"40330_CR45","first-page":"5538","volume-title":"Proceedings of 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)","author":"B Yang","year":"2024","unstructured":"Yang B, Tang C, Zhao K, Xiao C, Lin C. Effective distillation of table-based reasoning ability from LLMs. In: Proceedings of 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). 2024, 5538\u20135550"},{"key":"40330_CR46","volume-title":"HELLaMA: LLaMA-based table to text generation by highlighting the important evidence","author":"J Bian","year":"2023","unstructured":"Bian J, Qin X, Zou W, Huang M, Zhang W. HELLaMA: LLaMA-based table to text generation by highlighting the important evidence. 2023, arXiv preprint arXiv: 2311.08896"},{"key":"40330_CR47","volume-title":"ProTrix: building models for planning and reasoning over tables with sentence context","author":"Z Wu","year":"2024","unstructured":"Wu Z, Feng Y. ProTrix: building models for planning and reasoning over tables with sentence context. 2024, arXiv preprint arXiv: 2403.02177"},{"key":"40330_CR48","volume-title":"TableLLM: enabling tabular data manipulation by LLMs in real office usage scenarios","author":"X Zhang","year":"2024","unstructured":"Zhang X, Zhang J, Ma Z, Li Y, Zhang B, Li G, Yao Z, Xu K, Zhou J, Zhang-Li D, Yu J, Zhao S, Li J, Tang J. TableLLM: enabling tabular data manipulation by LLMs in real office usage scenarios. 2024, arXiv preprint arXiv: 2403.19318"},{"key":"40330_CR49","doi-asserted-by":"publisher","first-page":"602","DOI":"10.18653\/v1\/2022.emnlp-main.39","volume-title":"Proceedings of 2022 Conference on Empirical Methods in Natural Language Processing","author":"T Xie","year":"2022","unstructured":"Xie T, Wu C H, Shi P, Zhong R, Scholak T, Yasunaga M, Wu C S, Zhong M, Yin P, Wang S I, Zhong V, Wang B, Li C, Boyle C, Ni A, Yao Z, Radev D, Xiong C, Kong L, Zhang R, Smith N A, Zettlemoyer L, Yu T. UnifiedSKG: unifying and multi-tasking structured knowledge grounding with text-to-text language models. In: Proceedings of 2022 Conference on Empirical Methods in Natural Language Processing. 2022, 602\u2013631"},{"key":"40330_CR50","first-page":"450","volume-title":"Proceedings of 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)","author":"T Liu","year":"2024","unstructured":"Liu T, Wang F, Chen M. Rethinking tabular data understanding with large language models. In: Proceedings of 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024, 450\u2013482"},{"key":"40330_CR51","volume-title":"Using LLM to select the right SQL query from candidates","author":"Z Li","year":"2024","unstructured":"Li Z, Xie T. Using LLM to select the right SQL query from candidates. 2024, arXiv preprint arXiv: 2401.02115"},{"key":"40330_CR52","first-page":"2505","volume-title":"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)","author":"Y Gan","year":"2021","unstructured":"Gan Y, Chen X, Huang Q, Purver M, Woodward J R, Xie J, Huang P. Towards robustness of text-to-SQL models against synonym substitution. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021, 2505\u20132515"},{"key":"40330_CR53","doi-asserted-by":"publisher","first-page":"1120","DOI":"10.18653\/v1\/2023.findings-eacl.83","volume-title":"Proceedings of the Association for Computational Linguistics: EACL 2023","author":"W Chen","year":"2023","unstructured":"Chen W. Large language models are few(1)-shot table reasoners. In: Proceedings of the Association for Computational Linguistics: EACL 2023. 2023, 1120\u20131130"},{"key":"40330_CR54","doi-asserted-by":"publisher","first-page":"14935","DOI":"10.18653\/v1\/2023.findings-emnlp.996","volume-title":"Proceedings of the Association for Computational Linguistics: EMNLP 2023","author":"L Nan","year":"2023","unstructured":"Nan L, Zhao Y, Zou W, Ri N, Tae J, Zhang E, Cohan A, Radev D. Enhancing text-to-SQL capabilities of large language models: a study on prompt design strategies. In: Proceedings of the Association for Computational Linguistics: EMNLP 2023. 2023, 14935\u201314956"},{"key":"40330_CR55","volume-title":"TAP4LLM: table provider on sampling, augmenting, and packing semi-structured data for large language model reasoning","author":"Y Sui","year":"2023","unstructured":"Sui Y, Zou J, Zhou M, He X, Du L, Han S, Zhang D. TAP4LLM: table provider on sampling, augmenting, and packing semi-structured data for large language model reasoning. 2023, arXiv preprint arXiv: 2312.09039"},{"key":"40330_CR56","doi-asserted-by":"publisher","first-page":"3501","DOI":"10.18653\/v1\/2023.findings-emnlp.227","volume-title":"Proceedings of the Association for Computational Linguistics: EMNLP 2023","author":"H Zhang","year":"2023","unstructured":"Zhang H, Cao R, Chen L, Xu H, Yu K. ACT-SQL: in-context learning for text-to-SQL with automatically-generated chain-of-thought. In: Proceedings of the Association for Computational Linguistics: EMNLP 2023. 2023, 3501\u20133532"},{"key":"40330_CR57","first-page":"464","volume-title":"Proceedings of 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track)","author":"D Min","year":"2024","unstructured":"Min D, Hu N, Jin R, Lin N, Chen J, Chen Y, Li Y, Qi G, Li Y, Li N, Wang Q. Exploring the impact of table-to-text methods on augmenting LLM-based question answering with domain hybrid data. In: Proceedings of 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track). 2024, 464\u2013482"},{"key":"40330_CR58","volume-title":"TableQAKit: a comprehensive and practical toolkit for table-based question answering","author":"F Lei","year":"2023","unstructured":"Lei F, Luo T, Yang P, Liu W, Liu H, Lei J, Huang Y, Wei Y, He S, Zhao J, Liu K. TableQAKit: a comprehensive and practical toolkit for table-based question answering. 2023, arXiv preprint arXiv: 2310.15075"},{"key":"40330_CR59","doi-asserted-by":"publisher","first-page":"14054","DOI":"10.18653\/v1\/2023.emnlp-main.868","volume-title":"Proceedings of 2023 Conference on Empirical Methods in Natural Language Processing","author":"M Kothyari","year":"2023","unstructured":"Kothyari M, Dhingra D, Sarawagi S, Chakrabarti S. CRUSH4SQL: collective retrieval using schema hallucination for Text2SQL. In: Proceedings of 2023 Conference on Empirical Methods in Natural Language Processing. 2023, 14054\u201314066"},{"key":"40330_CR60","volume-title":"Proceedings of the 12th International Conference on Learning Representations","author":"K Kong","year":"2024","unstructured":"Kong K, Zhang J, Shen Z, Srinivasan B, Lei C, Faloutsos C, Rangwala H, Karypis G. OpenTab: advancing large language models as open-domain table reasoners. In: Proceedings of the 12th International Conference on Learning Representations. 2024"},{"key":"40330_CR61","volume-title":"DB-GPT: empowering database interactions with private large language models","author":"S Xue","year":"2024","unstructured":"Xue S, Jiang C, Shi W, Cheng F, Chen K, Yang H, Zhang Z, He J, Zhang H, Wei G, Zhao W, Zhou F, Qi D, Yi H, Liu S, Chen F. DB-GPT: empowering database interactions with private large language models. 2024, arXiv preprint arXiv: 2312.17449"},{"key":"40330_CR62","volume-title":"DBCopilot: scaling natural language querying to massive databases","author":"T Wang","year":"2023","unstructured":"Wang T, Lin H, Han X, Sun L, Chen X, Wang H, Zeng Z. DBCopilot: scaling natural language querying to massive databases. 2023, arXiv preprint arXiv: 2312.03463"},{"key":"40330_CR63","volume-title":"MAC-SQL: multi-agent collaboration for text-to-SQL","author":"B Wang","year":"2023","unstructured":"Wang B, Ren C, Yang J, Liang X, Bai J, Zhang Q W, Yan Z, Li Z. MAC-SQL: multi-agent collaboration for text-to-SQL. 2023, arXiv preprint arXiv: 2312.11242"},{"key":"40330_CR64","volume-title":"EHRAgent: code empowers large language models for few-shot complex tabular reasoning on electronic health records","author":"W Shi","year":"2024","unstructured":"Shi W, Xu R, Zhuang Y, Yu Y, Zhang J, Wu H, Zhu Y, Ho J, Yang C, Wang M D. EHRAgent: code empowers large language models for few-shot complex tabular reasoning on electronic health records. 2024, arXiv preprint arXiv: 2401.07128"},{"key":"40330_CR65","volume-title":"Proceedings of the 41st International Conference on Machine Learning","author":"S Han","year":"2024","unstructured":"Han S, Yoon J, Arik S O, Pfister T. Large language models can automatically engineer features for few-shot tabular learning. In: Proceedings of the 41st International Conference on Machine Learning. 2024"},{"key":"40330_CR66","doi-asserted-by":"publisher","first-page":"14536","DOI":"10.18653\/v1\/2023.emnlp-main.897","volume-title":"Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing","author":"Y Cao","year":"2023","unstructured":"Cao Y, Chen S, Liu R, Wang Z, Fried D. API-assisted code generation for question answering on varied table structures. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023, 14536\u201314548"},{"key":"40330_CR67","doi-asserted-by":"publisher","first-page":"9237","DOI":"10.18653\/v1\/2023.emnlp-main.574","volume-title":"Proceedings of 2023 Conference on Empirical Methods in Natural Language Processing","author":"J Jiang","year":"2023","unstructured":"Jiang J, Zhou K, Dong Z, Ye K, Zhao X, Wen J R. StructGPT: a general framework for large language model to reason over structured data. In: Proceedings of 2023 Conference on Empirical Methods in Natural Language Processing. 2023, 9237\u20139251"},{"key":"40330_CR68","doi-asserted-by":"publisher","first-page":"4556","DOI":"10.18653\/v1\/2024.findings-naacl.284","volume-title":"Proceedings of the Association for Computational Linguistics: NAACL 2024","author":"L Nan","year":"2024","unstructured":"Nan L, Zhang E, Zou W, Zhao Y, Zhou W, Cohan A. On evaluating the integration of reasoning and action in LLM agents with database question answering. In: Proceedings of the Association for Computational Linguistics: NAACL 2024. 2024, 4556\u20134579"},{"issue":"8","key":"40330_CR69","doi-asserted-by":"publisher","first-page":"1981","DOI":"10.14778\/3659437.3659452","volume":"17","author":"Y Zhang","year":"2024","unstructured":"Zhang Y, Henkel J, Floratou A, Cahoon J, Deep S, Patel J M. ReAcTable: enhancing ReAct for table question answering. Proceedings of the VLDB Endowment, 2024, 17(8): 1981\u20131994","journal-title":"Proceedings of the VLDB Endowment"},{"issue":"12","key":"40330_CR70","doi-asserted-by":"publisher","first-page":"4361","DOI":"10.1007\/s13042-023-01898-3","volume":"14","author":"L Dou","year":"2023","unstructured":"Dou L, Gao Y, Pan M, Wang D, Che W, Lou J G, Zhan D. UNISAR: a unified structure-aware autoregressive language model for text-to-SQL semantic parsing. International Journal of Machine Learning and Cybernetics, 2023, 14(12): 4361\u20134376","journal-title":"International Journal of Machine Learning and Cybernetics"},{"key":"40330_CR71","first-page":"1800","volume-title":"Proceedings of the 36th International Conference on Neural Information Processing Systems","author":"J Wei","year":"2022","unstructured":"Wei J, Wang X, Schuurmans D, Bosma M, Ichter B, Xia F, Chi E H, Le Q V, Zhou D. Chain-of-thought prompting elicits reasoning in large language models. In: Proceedings of the 36th International Conference on Neural Information Processing Systems. 2022, 1800"},{"key":"40330_CR72","volume-title":"Document AI: Benchmarks, models and applications","author":"L Cui","year":"2021","unstructured":"Cui L, Xu Y, Lv T, Wei F. Document AI: Benchmarks, models and applications. 2021, arXiv preprint arXiv: 2111.08609"},{"key":"40330_CR73","volume-title":"TinyChart: efficient chart understanding with visual token merging and program-of-thoughts learning","author":"L Zhang","year":"2024","unstructured":"Zhang L, Hu A, Xu H, Yan M, Xu Y, Jin Q, Zhang J, Huang F. TinyChart: efficient chart understanding with visual token merging and program-of-thoughts learning. 2024, arXiv preprint arXiv: 2404.16635"},{"key":"40330_CR74","volume-title":"mChartQA: a universal benchmark for multimodal chart question answer based on vision-language alignment and reasoning","author":"J Wei","year":"2024","unstructured":"Wei J, Xu N, Chang G, Luo Y, Yu B, Guo R. mChartQA: a universal benchmark for multimodal chart question answer based on vision-language alignment and reasoning. 2024, arXiv preprint arXiv: 2404.01548"},{"key":"40330_CR75","doi-asserted-by":"publisher","first-page":"9102","DOI":"10.18653\/v1\/2024.acl-long.493","volume-title":"Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","author":"M Zheng","year":"2024","unstructured":"Zheng M, Feng X, Si Q, She Q, Lin Z, Jiang W, Wang W. Multimodal table understanding. In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024, 9102\u20139124"},{"issue":"4","key":"40330_CR76","doi-asserted-by":"publisher","first-page":"3055","DOI":"10.1007\/s10462-022-10248-8","volume":"56","author":"J Ni","year":"2023","unstructured":"Ni J, Young T, Pandelea V, Xue F, Cambria E. Recent advances in deep learning based dialogue systems: a systematic survey. Artificial Intelligence Review, 2023, 56(4): 3055\u20133155","journal-title":"Artificial Intelligence Review"},{"key":"40330_CR77","doi-asserted-by":"publisher","first-page":"4511","DOI":"10.18653\/v1\/P19-1443","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","author":"T Yu","year":"2019","unstructured":"Yu T, Zhang R, Yasunaga M, Tan Y C, Lin X V, Li S, Er H, Li I, Pang B, Chen T, Ji E, Dixit S, Proctor D, Shim S, Kraft J, Zhang V, Xiong C, Socher R, Radev D. SParC: cross-domain semantic parsing in context. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019, 4511\u20134523"},{"key":"40330_CR78","volume-title":"Retrieval-augmented generation for large language models: a survey","author":"Y Gao","year":"2024","unstructured":"Gao Y, Xiong Y, Gao X, Jia K, Pan J, Bi Y, Dai Y, Sun J, Guo Q, Wang M, Wang H. Retrieval-augmented generation for large language models: a survey. 2024, arXiv preprint arXiv: 2312.10997"},{"key":"40330_CR79","doi-asserted-by":"publisher","first-page":"5240","DOI":"10.18653\/v1\/2022.emnlp-main.350","volume-title":"Proceedings of 2022 Conference on Empirical Methods in Natural Language Processing","author":"L Dou","year":"2022","unstructured":"Dou L, Gao Y, Liu X, Pan M, Wang D, Che W, Zhan D, Kan M Y, Lou J G. Towards knowledge-intensive text-to-SQL semantic parsing with formulaic knowledge. In: Proceedings of 2022 Conference on Empirical Methods in Natural Language Processing. 2022, 5240\u20135253"},{"key":"40330_CR80","first-page":"932","volume-title":"Proceedings of 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"Z Jiang","year":"2022","unstructured":"Jiang Z, Mao Y, He P, Neubig G, Chen W. OmniTab: pretraining with natural and synthetic data for few-shot table-based question answering. In: Proceedings of 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022, 932\u2013942"},{"key":"40330_CR81","first-page":"1401","volume-title":"Proceedings of the 29th International Conference on Computational Linguistics","author":"G Zhao","year":"2022","unstructured":"Zhao G, Yang P. Table-based fact verification with self-labeled keypoint alignment. In: Proceedings of the 29th International Conference on Computational Linguistics. 2022, 1401\u20131411"},{"key":"40330_CR82","doi-asserted-by":"publisher","first-page":"8413","DOI":"10.18653\/v1\/2020.acl-main.745","volume-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","author":"P Yin","year":"2020","unstructured":"Yin P, Neubig G, Yih W T, Riedel S. TaBERT: pretraining for joint understanding of textual and tabular data. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020, 8413\u20138426"},{"key":"40330_CR83","volume-title":"Sparks of artificial general intelligence: early experiments with GPT-4","author":"S Bubeck","year":"2023","unstructured":"Bubeck S, Chandrasekaran V, Eldan R, Gehrke J, Horvitz E, Kamar E, Lee P, Lee Y T, Li Y, Lundberg S, Nori H, Palangi H, Ribeiro M T, Zhang Y. Sparks of artificial general intelligence: early experiments with GPT-4. 2023, arXiv preprint arXiv: 2303.12712"},{"key":"40330_CR84","doi-asserted-by":"publisher","first-page":"1174","DOI":"10.18653\/v1\/2021.findings-acl.100","volume-title":"Proceedings of the Association for Computational Linguistics: ACLIJCNLP 2021","author":"Q Liu","year":"2021","unstructured":"Liu Q, Yang D, Zhang J, Guo J, Zhou B, Lou J G. Awakening latent grounding from pretrained language models for semantic parsing. In: Proceedings of the Association for Computational Linguistics: ACLIJCNLP 2021. 2021, 1174\u20131189"},{"key":"40330_CR85","volume-title":"Proceedings of the Eleventh International Conference on Learning Representations","author":"W Yu","year":"2023","unstructured":"Yu W, Iter D, Wang S, Xu Y, Ju M, Sanyal S, Zhu C, Zeng M, Jiang M. Generate rather than retrieve: large language models are strong context generators. In: Proceedings of the Eleventh International Conference on Learning Representations. 2023"},{"key":"40330_CR86","first-page":"22631","volume-title":"Proceedings of the 40th International Conference on Machine Learning","author":"S Longpre","year":"2023","unstructured":"Longpre S, Hou L, Vu T, Webson A, Chung H W, Tay Y, Zhou D, Le Q V, Zoph B, Wei J, Roberts A. The flan collection: designing data and methods for effective instruction tuning. In: Proceedings of the 40th International Conference on Machine Learning. 2023, 22631\u201322648"},{"key":"40330_CR87","volume-title":"Proceedings of the 12th International Conference on Learning Representations","author":"W Liu","year":"2024","unstructured":"Liu W, Zeng W, He K, Jiang Y, He J. What makes good data for alignment? A comprehensive study of automatic data selection in instruction tuning. In: Proceedings of the 12th International Conference on Learning Representations. 2024"},{"key":"40330_CR88","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-emnlp.195","volume-title":"Data diversity matters for robust instruction tuning","author":"A Bukharin","year":"2024","unstructured":"Bukharin A, Zhao T. Data diversity matters for robust instruction tuning. 2024, arXiv preprint arXiv: 2311.14736"},{"key":"40330_CR89","volume-title":"RegMix: data mixture as regression for language model pre-training","author":"Q Liu","year":"2024","unstructured":"Liu Q, Zheng X, Muennighoff N, Zeng G, Dou L, Pang T, Jiang J, Lin M. RegMix: data mixture as regression for language model pre-training. 2024, arXiv preprint arXiv: 2407.01492"},{"key":"40330_CR90","volume-title":"WizardLM: empowering large language models to follow complex instructions","author":"C Xu","year":"2023","unstructured":"Xu C, Sun Q, Zheng K, Geng X, Zhao P, Feng J, Tao C, Jiang D. WizardLM: empowering large language models to follow complex instructions. 2023, arXiv preprint arXiv: 2304.12244"},{"key":"40330_CR91","volume-title":"Proceedings of the 12th International Conference on Learning Representations","author":"Z Luo","year":"2024","unstructured":"Luo Z, Xu C, Zhao P, Sun Q, Geng X, Hu W, Tao C, Ma J, Lin Q, Jiang D. WizardCoder: empowering code large language models with evol-instruct. In: Proceedings of the 12th International Conference on Learning Representations. 2024"},{"key":"40330_CR92","doi-asserted-by":"publisher","first-page":"8123","DOI":"10.18653\/v1\/2023.acl-long.452","volume-title":"Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","author":"T Y Chang","year":"2023","unstructured":"Chang T Y, Jia R. Data curation alone can stabilize in-context learning. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023, 8123\u20138144"},{"key":"40330_CR93","doi-asserted-by":"publisher","first-page":"13569","DOI":"10.18653\/v1\/2023.emnlp-main.837","volume-title":"Proceedings of 2023 Conference on Empirical Methods in Natural Language Processing","author":"J Tonglet","year":"2023","unstructured":"Tonglet J, Reusens M, Borchert P, Baesens B. SEER: a knapsack approach to exemplar selection for in-context HybridQA. In: Proceedings of 2023 Conference on Empirical Methods in Natural Language Processing. 2023, 13569\u201313583"},{"key":"40330_CR94","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-emnlp.65","volume-title":"Improving demonstration diversity by human-free fusing for text-to-SQL","author":"D Wang","year":"2024","unstructured":"Wang D, Dou L, Zhang X, Zhu Q, Che W. Improving demonstration diversity by human-free fusing for text-to-SQL. 2024, arXiv preprint arXiv: 2402.10663"},{"key":"40330_CR95","first-page":"1802","volume-title":"Proceedings of the 37th International Conference on Neural Information Processing Systems","author":"Y Xie","year":"2023","unstructured":"Xie Y, Kawaguchi K, Zhao Y, Zhao X, Kan M Y, He J, Xie M Q. Self-evaluation guided beam search for reasoning. In: Proceedings of the 37th International Conference on Neural Information Processing Systems. 2023, 1802"},{"key":"40330_CR96","volume-title":"Proceedings of the 12th International Conference on Learning Representations","author":"C Yang","year":"2024","unstructured":"Yang C, Wang X, Lu Y, Liu H, Le Q V, Zhou D, Chen X. Large language models as optimizers. In: Proceedings of the 12th International Conference on Learning Representations. 2024"},{"key":"40330_CR97","volume-title":"Proceedings of the 11th International Conference on Learning Representations","author":"S Yao","year":"2023","unstructured":"Yao S, Zhao J, Yu D, Du N, Shafran I, Narasimhan K R, Cao Y. ReAct: synergizing reasoning and acting in language models. In: Proceedings of the 11th International Conference on Learning Representations. 2023"},{"key":"40330_CR98","volume-title":"Proceedings of the 12th International Conference on Learning Representations","author":"T Cai","year":"2024","unstructured":"Cai T, Wang X, Ma T, Chen X, Zhou D. Large language models as tool makers. In: Proceedings of the 12th International Conference on Learning Representations. 2024"},{"key":"40330_CR99","first-page":"517","volume-title":"Proceedings of the 37th International Conference on Neural Information Processing Systems","author":"S Yao","year":"2023","unstructured":"Yao S, Yu D, Zhao J, Shafran I, Griffiths T L, Cao Y, Narasimhan K R. Tree of thoughts: deliberate problem solving with large language models. In: Proceedings of the 37th International Conference on Neural Information Processing Systems. 2023, 517"}],"container-title":["Frontiers of Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11704-024-40330-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11704-024-40330-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11704-024-40330-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T03:58:19Z","timestamp":1737604699000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11704-024-40330-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,22]]},"references-count":99,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["40330"],"URL":"https:\/\/doi.org\/10.1007\/s11704-024-40330-z","relation":{},"ISSN":["2095-2228","2095-2236"],"issn-type":[{"value":"2095-2228","type":"print"},{"value":"2095-2236","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,22]]},"assertion":[{"value":"2 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Competing interests The authors declare that they have no competing interests or financial conflicts to disclose.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics"}}],"article-number":"199348"}}