# MiningGPT -- A Domain-Specific Large Language Model for the Mining Industry

arXiv: https://arxiv.org/abs/2412.01189 · HTML: https://mlsanity.com/paper/2412.01189/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 6 references that are in the map.
- First posted on arXiv 2 Dec 2024.

## Builds on (most cited first, 3 of 3)

- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus](https://mlsanity.com/paper/2104.08758/) (2021)
- [Large Language Models in Finance: A Survey](https://mlsanity.com/paper/2311.10723/) (2023)

## Abstract (arXiv)

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.