Summary of Fine-tuning and Evaluating Open-source Large Language Models For the Army Domain, by Daniel C. Ruiz and John Sell
Fine-Tuning and Evaluating Open-Source Large Language Models for the Army Domain
by Daniel C. Ruiz, John Sell
First submitted to arxiv on: 27 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the potential for adapting Large Language Models (LLMs) for use in the Army domain, focusing on fine-tuning open-source models to address their lack of domain-specificity. The authors introduce TRACLM, a family of LLMs developed by The Research and Analysis Center (TRAC), Army Futures Command (AFC). They demonstrate three generations of TRACLM, each improved through refinement of the training pipeline, with enhanced capabilities on Army tasks and use cases. To evaluate the Army domain-specific knowledge of LLMs, the authors develop MilBench, an extensible software framework using tasks derived from doctrine and assessments. The paper presents preliminary results, models, methods, and recommendations for creating TRACLM and MilBench, informing development across the Department of Defense (DoD) and senior leader decisions on artificial intelligence integration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make Large Language Models work better for the Army. Right now, these models aren’t very good at understanding army-specific words and phrases. To fix this, people are fine-tuning the models to make them more useful. The authors of the paper created three versions of a model called TRACLM, each one getting better at doing tasks that the Army needs help with. They also developed a new way to test how well these models understand army-specific things, which they call MilBench. This helps figure out what makes a model good for army use and can guide decisions about using artificial intelligence in the military. |
Keywords
* Artificial intelligence * Fine tuning
