MooreZheng
commented
1 month ago If anyone has questions regarding this issue, please feel free to leave a message here. We would also appreciate it if new members could introduce themselves to the community.
What would you like to be added/modified: Research benchmarks for evaluating LLM and LLM Agent Develop a personalized LLM Agent using lifelong learning on the KubeEdge-lanvs edge-cloud collaborative platform
Why is this needed: Large Language Models (LLMs) have garnered widespread attention due to their exceptional reasoning abilities and zero-shot capabilities. Among these, the LLM Agent is viewed as a significant practical application of LLMs in the physical world. An LLM Agent can achieve various complex tasks in the physical world through task planning, tool usage, self-reflection, and task execution. This project aims to develop a personalized LLM Agent by utilizing a cloud-edge collaborative framework, combining responses from large cloud-based models with those generated from privacy-sensitive data on edge devices. We plan to develop a personalized LLM Agent based on the KubeEdge-lanvs cloud-edge collaborative platform for lifelong learning. This system will be capable of integrating the generalization capabilities of large cloud-based LLMs with personalized user data on edge devices to generate high-quality and personalized responses.
Recommended Skills: LLMs, Python, KubeEdge-Ianvs
Useful links: Introduction to Ianvs Install of Ianvs and Introduction to Lifelong Learning HuggingGPT: Solving AI tasks with chatgpt and its friends in hugging face, NeurIPS '24 TaskBench: Benchmarking Large Language Models for Task Automation