mlsanityData to 14 Oct 2026

Domain Adapted Large Language Models for Additive Manufacturing

arXiv 2603.22017 ↗First posted 23 Mar 2026cs.LGcited by 0

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Abstract (arXiv)

This work presents a collection of multi-modal domain adapted large language models built upon the instruction tuned variants of open weight models (Gemma 3, Qwen 3, Gemma 4) using a relatively small dataset of around 50 million tokens. The dataset consists of open-access additive manufacturing journal articles with data extracted for the domain adaptive pretraining and visual instruction tuning processes. Various stages of the developed model are evaluated with the Additive-Manufacturing-Benchmark which consists of additive manufacturing domain specific tasks compiled published resources. Domain adapted and instruction tuned models exhibit proficiency in both language and vision based tasks, achieving accuracies upwards of 90% in general additive manufacturing knowledge. This domain adaptive pretraining and instruction tuning strategy outline an accessible specialization method for large language models to a domain such as additive manufacturing.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 60 of its 114 references that are in the map. First posted on arXiv 23 Mar 2026.

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References · 60

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38 more
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