MIMIR: A Streamlined Platform for Personalized Agent Tuning in Domain Expertise
<|reference_start|>MIMIR: A Streamlined Platform for Personalized Agent Tuning in Domain Expertise: Recently, large language models (LLMs) have evolved into interactive agents, proficient in planning, tool use, and task execution across a wide variety of tasks. However, without specific agent tuning, open-source models like LLaMA currently struggle to match the efficiency of GPT- 4, particularly given the scarcity of agent-tuning datasets for fine-tuning. In response, we introduce \textsc{Mimir}: a streamlined platform offering a customizable pipeline that enables users to leverage both private knowledge and publicly available, legally compliant datasets at scale for \textbf{personalized agent tuning}. Additionally, \textsc{Mimir} supports the generation of general instruction-tuning datasets from the same input. This dual capability ensures that language agents developed through the platform possess both specific agent abilities and general competencies. \textsc{Mimir} integrates these features into a cohesive end-to-end platform, facilitating everything from the uploading of personalized files to one-click agent fine-tuning.<|reference_end|>
@article{deng2024mimir:,
    title={MIMIR: A Streamlined Platform for Personalized Agent Tuning in Domain
  Expertise},
    author={Chunyuan Deng, Xiangru Tang, Yilun Zhao, Hanming Wang, Haoran Wang,
  Wangchunshu Zhou, Arman Cohan, Mark Gerstein},
    journal={arXiv preprint arXiv:2404.04285},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.04285},
    primaryClass={cs.CL cs.AI}
}