{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T06:22:54Z","timestamp":1774419774340,"version":"3.50.1"},"reference-count":30,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,4,6]],"date-time":"2025-04-06T00:00:00Z","timestamp":1743897600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,4,6]],"date-time":"2025-04-06T00:00:00Z","timestamp":1743897600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,4,6]]},"DOI":"10.1109\/icassp49660.2025.10888696","type":"proceedings-article","created":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T13:56:59Z","timestamp":1741787819000},"page":"1-5","source":"Crossref","is-referenced-by-count":0,"title":["VisQ2SQL: Towards SQL-Driven Data Visualization via LLMs-Grounded Preference Learning"],"prefix":"10.1109","author":[{"given":"Shengze","family":"Shi","sequence":"first","affiliation":[{"name":"Institute of Software Chinese Academy of Sciences,State Key Laboratory of Intelligent Game,Beijing,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Ren","sequence":"additional","affiliation":[{"name":"Institute of Software Chinese Academy of Sciences,State Key Laboratory of Intelligent Game,Beijing,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Hu","sequence":"additional","affiliation":[{"name":"Institute of Software Chinese Academy of Sciences,State Key Laboratory of Intelligent Game,Beijing,China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3654992"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.visinf.2024.04.003"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/tvcg.2024.3374571"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3301275.3302270"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/2807442.2807478"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2017.2744684"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/2984511.2984588"},{"key":"ref8","article-title":"nvbench: A large-scale synthesized dataset for cross-domain natural language to visualization task","author":"Luo","year":"2021"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457261"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2019.2934785"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2018.2865240"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2018.00019"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2020.3030378"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2021.3114848"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539330"},{"key":"ref16","article-title":"Language models are few-shot learners","volume-title":"Proceedings of the 34th Annual Conference on Neural Information Processing Systems","author":"Brown"},{"key":"ref17","article-title":"Emergent abilities of large language models","author":"Wei","year":"2022"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-demo.11"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3613904.3642943"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2023.3326585"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-industry.64"},{"key":"ref22","article-title":"Visualization generation with large language models: An evaluation","author":"Li","year":"2024"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3274199"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.14778\/3641204.3641221"},{"key":"ref25","article-title":"C3: Zero-shot text-to-sql with chatgpt","author":"Dong","year":"2023"},{"key":"ref26","article-title":"Din-sql: Decomposed in-context learning of text-to-sql with self-correction","volume-title":"Proceedings of the 38th Annual Conference on Neural Information Processing Systems","volume":"36","author":"Pourreza"},{"key":"ref27","article-title":"Direct preference optimization: Your language model is secretly a reward model","volume-title":"Proceedings of the 38th Annual Conference on Neural Information Processing Systems","volume":"36","author":"Rafailov"},{"issue":"140","key":"ref28","first-page":"1","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"Raffel","year":"2020","journal-title":"Journal of Machine Learning Research"},{"key":"ref29","article-title":"Code llama: Open foundation models for code","author":"Roziere","year":"2023"},{"key":"ref30","article-title":"Llama 2: Open foundation and fine-tuned chat models","author":"Touvron","year":"2023"}],"event":{"name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","location":"Hyderabad, India","start":{"date-parts":[[2025,4,6]]},"end":{"date-parts":[[2025,4,11]]}},"container-title":["ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10887540\/10887541\/10888696.pdf?arnumber=10888696","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T05:24:38Z","timestamp":1774416278000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10888696\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,6]]},"references-count":30,"URL":"https:\/\/doi.org\/10.1109\/icassp49660.2025.10888696","relation":{},"subject":[],"published":{"date-parts":[[2025,4,6]]}}}