Summary of Knowledge Fusion by Evolving Weights Of Language Models, By Guodong Du et al.
Knowledge Fusion By Evolving Weights of Language Models
by Guodong Du, Jing Li, Hanting Liu, Runhua Jiang, Shuyang Yu, Yifei Guo, Sim Kuan Goh, Ho-Kin Tang
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Neural and Evolutionary Computing (cs.NE)
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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 paper proposes a novel approach to fine-tuning pre-trained language models, specifically large language models, by integrating multiple models from diverse training scenarios into a unified model. The authors develop a knowledge fusion method called Evolver, inspired by evolutionary algorithms, which does not require further training or additional training data. This method involves aggregating the weights of different language models into a population and generating offspring models through mutation and crossover operations. These offspring models are then evaluated against their parents, allowing for the preservation of those models that show enhanced performance on development datasets. The authors demonstrate the effectiveness of Evolver by comparing it to previous state-of-the-art models on mainstream language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a way to make large language models work better together. By combining different models and testing them against each other, they create a new model that can perform well across many different types of data. This is helpful because training these large models requires a lot of computer power and can be unpredictable in terms of how well they’ll do on certain tasks. The new approach, called Evolver, doesn’t need any extra training or data to work – it just combines the strengths of multiple models. |
Keywords
» Artificial intelligence » Fine tuning
