Summary of Fedcollm: a Parameter-efficient Federated Co-tuning Framework For Large and Small Language Models, by Tao Fan et al.
FedCoLLM: A Parameter-Efficient Federated Co-tuning Framework for Large and Small Language Models
by Tao Fan, Yan Kang, Guoqiang Ma, Lixin Fan, Kai Chen, Qiang Yang
First submitted to arxiv on: 18 Nov 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 In this paper, researchers aim to bridge the gap between Large Language Models (LLMs) and Small Language Models (SLMs) by developing a novel federated framework called FedCoLLM. The goal is to adaptively transfer knowledge from LLMs to SLMs while enriching the LLMs with domain-specific insights from clients. To achieve this, FedCoLLM uses lightweight adapters and SLMs to facilitate knowledge exchange between server and clients, respecting data privacy and minimizing overhead. Experimental results show significant performance improvements for clients’ SLMs with assistance from LLMs, while LLMs enhanced via FedCoLLM perform similarly to those fine-tuned on client data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to help language models work better together. Currently, large and small language models are not working together as well as they could be. The researchers propose a new method called FedCoLLM that helps the large model learn from the small models and vice versa. This is important because it can help make the models more accurate and efficient. The paper shows that this method works well for several different tasks, like generating text. |
