@inproceedings{imperial-etal-2026-rethinking,
title = "Rethinking Standard Compliance in the {LLM} Era",
author = "Imperial, Joseph Marvin and
Jones, Matthew D and
Tayyar Madabushi, Harish",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.245/",
pages = "5156--5175",
ISBN = "979-8-89176-462-0",
abstract = "We are witnessing an emerging paradigm shift in which the adoption of large language models (LLMs) has increased significantly across high-stakes, standards-driven industries, particularly in engineering, healthcare, education, and law. In line with EMNLP 2026{'}s theme of ``New Missions for NLP Research'', this position paper aims to initiate civil discussion on how the use of LLMs is shaping the future of standard compliance and introduces standard alignment, a targeted research direction towards making LLMs verifiably comply with domain standards. To support the discussion, we propose the Criticality and Compliance Capabilities Framework (C3F) to assess the criticality levels of various standards and pair them with the current compliance capabilities of state-of-the-art LLMs as evidenced by the literature. Finally, we argue that advancing standard alignment is key to ensuring safe and trustworthy integration of LLMs across standards-driven industries."
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<abstract>We are witnessing an emerging paradigm shift in which the adoption of large language models (LLMs) has increased significantly across high-stakes, standards-driven industries, particularly in engineering, healthcare, education, and law. In line with EMNLP 2026’s theme of “New Missions for NLP Research”, this position paper aims to initiate civil discussion on how the use of LLMs is shaping the future of standard compliance and introduces standard alignment, a targeted research direction towards making LLMs verifiably comply with domain standards. To support the discussion, we propose the Criticality and Compliance Capabilities Framework (C3F) to assess the criticality levels of various standards and pair them with the current compliance capabilities of state-of-the-art LLMs as evidenced by the literature. Finally, we argue that advancing standard alignment is key to ensuring safe and trustworthy integration of LLMs across standards-driven industries.</abstract>
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%0 Conference Proceedings
%T Rethinking Standard Compliance in the LLM Era
%A Imperial, Joseph Marvin
%A Jones, Matthew D.
%A Tayyar Madabushi, Harish
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F imperial-etal-2026-rethinking
%X We are witnessing an emerging paradigm shift in which the adoption of large language models (LLMs) has increased significantly across high-stakes, standards-driven industries, particularly in engineering, healthcare, education, and law. In line with EMNLP 2026’s theme of “New Missions for NLP Research”, this position paper aims to initiate civil discussion on how the use of LLMs is shaping the future of standard compliance and introduces standard alignment, a targeted research direction towards making LLMs verifiably comply with domain standards. To support the discussion, we propose the Criticality and Compliance Capabilities Framework (C3F) to assess the criticality levels of various standards and pair them with the current compliance capabilities of state-of-the-art LLMs as evidenced by the literature. Finally, we argue that advancing standard alignment is key to ensuring safe and trustworthy integration of LLMs across standards-driven industries.
%U https://aclanthology.org/2026.findings-emnlp.245/
%P 5156-5175
Markdown (Informal)
[Rethinking Standard Compliance in the LLM Era](https://aclanthology.org/2026.findings-emnlp.245/) (Imperial et al., Findings 2026)
ACL
- Joseph Marvin Imperial, Matthew D Jones, and Harish Tayyar Madabushi. 2026. Rethinking Standard Compliance in the LLM Era. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 5156–5175, Budapest, Hungary. Association for Computational Linguistics.