Summary of Fedeval-llm: Federated Evaluation Of Large Language Models on Downstream Tasks with Collective Wisdom, by Yuanqin He et al.
FedEval-LLM: Federated Evaluation of Large Language Models on Downstream Tasks with Collective Wisdom
by Yuanqin He, Yan Kang, Lixin Fan, Qiang Yang
First submitted to arxiv on: 18 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 Federated Evaluation framework of Large Language Models (FedEval-LLM) addresses the challenges in evaluating large language models (LLMs) in federated learning settings. The traditional methods are limited, and automatic evaluation methods face risks of data leakage and suboptimal performance. FedEval-LLM leverages a consortium of personalized LLMs as referees to provide domain knowledge and collective evaluation capability. Experimental results show significant improvement in the evaluation capability of personalized models on downstream tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FedEval-LLM is a new way to test how well language models work. Right now, we have limited ways to evaluate these models, which can lead to problems. This framework helps by using many small models together to make a better evaluation. It works by having each model “referee” the others and then combining their scores. This makes it more accurate and reliable. |
Keywords
» Artificial intelligence » Federated learning
