@inproceedings{dai-2026-quilt,
title = "{QUILT}: Query{--}User Interaction Linguistic Taxonomy",
author = "Dai, Yuyang",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.aacl-long.138/",
pages = "3018--3035",
ISBN = "979-8-89176-439-2",
abstract = "Large language models are used by people with very different expertise levels, but existing work studies what users ask more than who is asking. We introduce QUILT (Query User Interaction Linguistic Taxonomy) , a two-dimensional framework that jointly characterizes LLM conversations by task type and user expertise level. Applying QUILT to 3,000 WildChat dialogues, we construct the first large-scale corpus linking expertise level to task-level interaction patterns in open-ended LLM conversations. Corpus analysis reveals clear expertise-dependent asymmetries: debugging queries are strongly associated with expert users, novice users rarely engage in reasoning-intensive tasks, and expert queries are substantially longer across all task types. These patterns are statistically significant and recoverable by automatic classifiers, indicating that expertise differences are systematically reflected in conversational language. Together, QUILT and the QUILT Corpus provide an empirical foundation for studying user heterogeneity in LLM interaction and for developing expertise-aware response strategies."
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%0 Conference Proceedings
%T QUILT: Query–User Interaction Linguistic Taxonomy
%A Dai, Yuyang
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-439-2
%F dai-2026-quilt
%X Large language models are used by people with very different expertise levels, but existing work studies what users ask more than who is asking. We introduce QUILT (Query User Interaction Linguistic Taxonomy) , a two-dimensional framework that jointly characterizes LLM conversations by task type and user expertise level. Applying QUILT to 3,000 WildChat dialogues, we construct the first large-scale corpus linking expertise level to task-level interaction patterns in open-ended LLM conversations. Corpus analysis reveals clear expertise-dependent asymmetries: debugging queries are strongly associated with expert users, novice users rarely engage in reasoning-intensive tasks, and expert queries are substantially longer across all task types. These patterns are statistically significant and recoverable by automatic classifiers, indicating that expertise differences are systematically reflected in conversational language. Together, QUILT and the QUILT Corpus provide an empirical foundation for studying user heterogeneity in LLM interaction and for developing expertise-aware response strategies.
%U https://aclanthology.org/2026.aacl-long.138/
%P 3018-3035
Markdown (Informal)
[QUILT: Query–User Interaction Linguistic Taxonomy](https://aclanthology.org/2026.aacl-long.138/) (Dai, AACL-IJCNLP 2026)
ACL
- Yuyang Dai. 2026. QUILT: Query–User Interaction Linguistic Taxonomy. In Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3018–3035, Hengqin, China. Association for Computational Linguistics.