Summary of Language Models As Continuous Self-evolving Data Engineers, by Peidong Wang et al.
Language Models as Continuous Self-Evolving Data Engineers
by Peidong Wang, Ming Wang, Zhiming Ma, Xiaocui Yang, Shi Feng, Daling Wang, Yifei Zhang, Kaisong Song
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large Language Models (LLMs) have achieved impressive results on various tasks, but their potential is limited by the lack of high-quality training data. To overcome this challenge, a novel paradigm called LANCE enables LLMs to train themselves by generating, cleaning, reviewing, and annotating data with preference information. This approach shows that LLMs can serve as continuous self-evolving data engineers, significantly reducing the time and cost of post-training data construction. Through iterative fine-tuning on Qwen2 series models, LANCE demonstrates its effectiveness across various tasks, maintaining high-quality data generation and continuously improving model performance. Across multiple benchmark dimensions, LANCE results in an average score enhancement of 3.64 for Qwen2-7B and 1.75 for Qwen2-7B-Instruct. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are very good at doing tasks, but they need better training data to get even better. A new way called LANCE lets the models make their own training data, which is a game-changer. This means that the models can learn faster and more efficiently without needing as much help from humans. The results show that LANCE makes the models work better and better over time. |
Keywords
» Artificial intelligence » Fine tuning
