Summary of Graphlora: Empowering Llms Fine-tuning Via Graph Collaboration Of Moe, by Ting Bai et al.
GraphLoRA: Empowering LLMs Fine-Tuning via Graph Collaboration of MoE
by Ting Bai, Yue Yu, Le Huang, Zenan Xu, Zhe Zhao, Chuan Shi
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 proposed GraphLoRA framework is a novel MoE-based approach for fine-tuning large language models (LLMs) that leverages graph neural networks (GNNs) to capture collaboration signals among experts. The method addresses the issue of instability in LLMs due to the imbalance load problem of MoE by designing a graph router function that enables all experts to share information and understand input knowledge through aggregating operations. Two novel coordination strategies are also introduced: the Poisson distribution-based distinction strategy and the Normal distribution-based load balance strategy. The effectiveness of GraphLoRA is demonstrated through extensive experiments on four real-world datasets, showcasing its benefits in parameter-efficient fine-tuning of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GraphLoRA is a new way to train large language models that helps them work better together. It uses special computer programs called graph neural networks (GNNs) to make the models share information and understand what’s important. This makes the models more stable and accurate, which is helpful when we need them to do tasks like understanding text or generating text. |
Keywords
» Artificial intelligence » Fine tuning » Parameter efficient
