Dawson, M.
, Lukens, S.
and Sharp, M.
(2025),
Advancing Technical Language Processing and Large Language Models in Industrial Applications: Insights from TLP COI Events in 2024, Special Publication (NIST SP), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.SP.1500-37, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=960066
(Accessed December 3, 2025)
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Advancing Technical Language Processing and Large Language Models in Industrial Applications: Insights from TLP COI Events in 2024
Published
Author(s)
, Sarah Lukens,
Abstract
It remains challenging to integrate artificial intelligence (AI) and natural language processing (NLP) into complex engineering systems. Popular models and techniques are designed for use with everyday, non-technical text, and therefore perform poorly when attempting to interpret language containing specialized terminology or context-specific meanings. Technical Language Processing (TLP) attempts to address these challenges by tailoring NLP approaches for use in technical and engineering domains. As a nascent field, community-driven events have played a crucial role in facilitating dialogue on emerging issues and potential solutions. In 2024, the TLP Community of Interest (COI), overseen by the National Institute of Standards and Technology (NIST), organized two events: a two day virtual workshop hosted by NIST, and a panel at the Annual Conference of the Prognostics and Health Management (PHM) Society. These two events provided opportunities for experts from academia, industry, and government to discuss the application of TLP and Large Language Models (LLMs) in industrial settings. This paper summarizes the key discussions and insights from the events, while highlighting the community's collective efforts to advance the application of TLP and LLMs in the industrial sector.Citation
Special Publication (NIST SP) - 1500-37
Report Number
1500-37
NIST Pub Series
Pub Type
NIST Pubs
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Keywords
Artificial intelligence, Data fusion, Engineering design, Industry, Information retrieval, Knowledge extraction, Large language models, Maintenance, Manufacturing, Natural language processing, Prognostics and health management, Retrieval-augmented generation, Standards, Technical language processing, Vision language models
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Created December 2, 2025