Summary of Mimir: a Streamlined Platform For Personalized Agent Tuning in Domain Expertise, by Chunyuan Deng et al.
MIMIR: A Streamlined Platform for Personalized Agent Tuning in Domain Expertise
by Chunyuan Deng, Xiangru Tang, Yilun Zhao, Hanming Wang, Haoran Wang, Wangchunshu Zhou, Arman Cohan, Mark Gerstein
First submitted to arxiv on: 3 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 introduces Mimir, a streamlined platform that enables users to leverage private knowledge and publicly available datasets for personalized agent tuning. The platform allows for customizable pipelines and generates general instruction-tuning datasets from input. This dual capability ensures language agents developed through Mimir possess specific abilities and general competencies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Mimir is a new tool that helps train language models like LLaMA to do tasks more efficiently, especially when using private data. The platform makes it easy to customize training pipelines and create special instruction datasets. This means the trained models can learn specific skills and general knowledge, making them more useful for various applications. |
Keywords
» Artificial intelligence » Instruction tuning » Llama
