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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Online-Zugang: | https://arxiv.org/abs/2412.01189 |
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| _version_ | 1866917853415866368 |
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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 |