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1. Verfasser: Demartini, Kurukulasooriya Fernando ana Gianluca
Format: Preprint
Veröffentlicht: 2024
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Online-Zugang:https://arxiv.org/abs/2412.01189
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author Demartini, Kurukulasooriya Fernando ana Gianluca
author_facet Demartini, Kurukulasooriya Fernando ana Gianluca
contents Recent advancements of generative LLMs (Large Language Models) have exhibited human-like language capabilities but have shown a lack of domain-specific understanding. Therefore, the research community has started the development of domain-specific LLMs for many domains. In this work we focus on discussing how to build mining domain-specific LLMs, as the global mining industry contributes significantly to the worldwide economy. We report on MiningGPT, a mining domain-specific instruction-following 7B parameter LLM model which showed a 14\% higher mining domain knowledge test score as compared to its parent model Mistral 7B instruct.
format Preprint
id arxiv_https___arxiv_org_abs_2412_01189
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle MiningGPT -- A Domain-Specific Large Language Model for the Mining Industry
Demartini, Kurukulasooriya Fernando ana Gianluca
Computation and Language
Recent advancements of generative LLMs (Large Language Models) have exhibited human-like language capabilities but have shown a lack of domain-specific understanding. Therefore, the research community has started the development of domain-specific LLMs for many domains. In this work we focus on discussing how to build mining domain-specific LLMs, as the global mining industry contributes significantly to the worldwide economy. We report on MiningGPT, a mining domain-specific instruction-following 7B parameter LLM model which showed a 14\% higher mining domain knowledge test score as compared to its parent model Mistral 7B instruct.
title MiningGPT -- A Domain-Specific Large Language Model for the Mining Industry
topic Computation and Language
url https://arxiv.org/abs/2412.01189