@inproceedings{harrasse-etal-2025-tinysql,
title = "{T}iny{SQL}: A Progressive Text-to-{SQL} Dataset for Mechanistic Interpretability Research",
author = "Harrasse, Abir and
Quirke, Philip and
Neo, Clement and
Nathawani, Dhruv and
Marks, Luke and
Abdullah, Amir",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1489/",
doi = "10.18653/v1/2025.emnlp-main.1489",
pages = "29256--29284",
ISBN = "979-8-89176-332-6",
abstract = "Mechanistic interpretability research faces a gap between analyzing simple circuits in toy tasks and discovering features in large models. To bridge this gap, we propose text-to-SQL generation as an ideal task to study, as it combines the formal structure of toy tasks with real-world complexity. We introduce TinySQL, a synthetic dataset, progressing from basic to advanced SQL operations, and train models ranging from 33M to 1B parameters to establish a comprehensive testbed for interpretability. We apply multiple complementary interpretability techniques, including Edge Attribution Patching and Sparse Autoencoders, to identify minimal circuits and components supporting SQL generation. We compare circuits for different SQL subskills, evaluating their minimality, reliability, and identifiability. Finally, we conduct a layerwise logit lens analysis to reveal how models compose SQL queries across layers: from intent recognition to schema resolution to structured generation. Our work provides a robust framework for probing and comparing interpretability methods in a structured, progressively complex setting."
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%0 Conference Proceedings
%T TinySQL: A Progressive Text-to-SQL Dataset for Mechanistic Interpretability Research
%A Harrasse, Abir
%A Quirke, Philip
%A Neo, Clement
%A Nathawani, Dhruv
%A Marks, Luke
%A Abdullah, Amir
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F harrasse-etal-2025-tinysql
%X Mechanistic interpretability research faces a gap between analyzing simple circuits in toy tasks and discovering features in large models. To bridge this gap, we propose text-to-SQL generation as an ideal task to study, as it combines the formal structure of toy tasks with real-world complexity. We introduce TinySQL, a synthetic dataset, progressing from basic to advanced SQL operations, and train models ranging from 33M to 1B parameters to establish a comprehensive testbed for interpretability. We apply multiple complementary interpretability techniques, including Edge Attribution Patching and Sparse Autoencoders, to identify minimal circuits and components supporting SQL generation. We compare circuits for different SQL subskills, evaluating their minimality, reliability, and identifiability. Finally, we conduct a layerwise logit lens analysis to reveal how models compose SQL queries across layers: from intent recognition to schema resolution to structured generation. Our work provides a robust framework for probing and comparing interpretability methods in a structured, progressively complex setting.
%R 10.18653/v1/2025.emnlp-main.1489
%U https://aclanthology.org/2025.emnlp-main.1489/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1489
%P 29256-29284
Markdown (Informal)
[TinySQL: A Progressive Text-to-SQL Dataset for Mechanistic Interpretability Research](https://aclanthology.org/2025.emnlp-main.1489/) (Harrasse et al., EMNLP 2025)
ACL