@inproceedings{hu-etal-2026-complete,
title = "Complete or Abstain? Aligning {SQL} Completion with User Preferences",
author = "Hu, Xintong and
Xie, Defeng and
Wang, Yuxiang and
Li, Chunyou and
Xie, Haining and
Liu, Hao and
Feng, Haozhe and
Li, Yang and
Huang, Danqing and
He, Debiao and
Yan, Xiao and
Jiang, Jiawei and
Chen, Peng",
editor = "Atanasova, Pepa and
Haffari, Gholamreza and
Wang, Minghan",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-industry.16/",
pages = "240--258",
ISBN = "979-8-89176-460-6",
abstract = "SQL code completion boosts developer productivity by predicting missing snippets from editing SQL contexts. However, existing solutions yield low acceptance since they cannot abstain when no suggestion is needed and lack SQL-specific training. To address both limitations, we introduce PACE-SQL, a preference-aligned framework that improves SQL completion accuracy and user preference alignment. We build two datasets with completion and abstention samples: WeSQL-1.5M from real-world interaction logs to capture authentic user preferences, and OpenSQL-FIM from public SQL corpora. We adopt continual pretraining and supervised fine-tuning to improve completion quality, and design a reward function in the reinforcement learning to enhance alignment with user preferences. Experiments show PACE-SQL improves Exact Match and Edit Similarity scores by 9.67{\%} and 7.52{\%} over the best baseline on WeSQL-1.5M and raises online user acceptance rate from 5{\%} to 29{\%}."
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<abstract>SQL code completion boosts developer productivity by predicting missing snippets from editing SQL contexts. However, existing solutions yield low acceptance since they cannot abstain when no suggestion is needed and lack SQL-specific training. To address both limitations, we introduce PACE-SQL, a preference-aligned framework that improves SQL completion accuracy and user preference alignment. We build two datasets with completion and abstention samples: WeSQL-1.5M from real-world interaction logs to capture authentic user preferences, and OpenSQL-FIM from public SQL corpora. We adopt continual pretraining and supervised fine-tuning to improve completion quality, and design a reward function in the reinforcement learning to enhance alignment with user preferences. Experiments show PACE-SQL improves Exact Match and Edit Similarity scores by 9.67% and 7.52% over the best baseline on WeSQL-1.5M and raises online user acceptance rate from 5% to 29%.</abstract>
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%0 Conference Proceedings
%T Complete or Abstain? Aligning SQL Completion with User Preferences
%A Hu, Xintong
%A Xie, Defeng
%A Wang, Yuxiang
%A Li, Chunyou
%A Xie, Haining
%A Liu, Hao
%A Feng, Haozhe
%A Li, Yang
%A Huang, Danqing
%A He, Debiao
%A Yan, Xiao
%A Jiang, Jiawei
%A Chen, Peng
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F hu-etal-2026-complete
%X SQL code completion boosts developer productivity by predicting missing snippets from editing SQL contexts. However, existing solutions yield low acceptance since they cannot abstain when no suggestion is needed and lack SQL-specific training. To address both limitations, we introduce PACE-SQL, a preference-aligned framework that improves SQL completion accuracy and user preference alignment. We build two datasets with completion and abstention samples: WeSQL-1.5M from real-world interaction logs to capture authentic user preferences, and OpenSQL-FIM from public SQL corpora. We adopt continual pretraining and supervised fine-tuning to improve completion quality, and design a reward function in the reinforcement learning to enhance alignment with user preferences. Experiments show PACE-SQL improves Exact Match and Edit Similarity scores by 9.67% and 7.52% over the best baseline on WeSQL-1.5M and raises online user acceptance rate from 5% to 29%.
%U https://aclanthology.org/2026.emnlp-industry.16/
%P 240-258
Markdown (Informal)
[Complete or Abstain? Aligning SQL Completion with User Preferences](https://aclanthology.org/2026.emnlp-industry.16/) (Hu et al., EMNLP 2026)
ACL
- Xintong Hu, Defeng Xie, Yuxiang Wang, Chunyou Li, Haining Xie, Hao Liu, Haozhe Feng, Yang Li, Danqing Huang, Debiao He, Xiao Yan, Jiawei Jiang, and Peng Chen. 2026. Complete or Abstain? Aligning SQL Completion with User Preferences. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 240–258, Budapest, Hungary. Association for Computational Linguistics.