Abstract
Most large language models are fine-tuned using either expensive human-annotated data or GPT-4 generated data which cannot guarantee performance in certain domains. We argue that although the web-crawled data often has formatting errors causing semantic inaccuracies, it can still serve as a valuable source for high-quality supervised fine-tuning in specific domains without relying on advanced models like GPT-4. To this end, we create a paired training dataset automatically by aligning web-crawled data with a smaller set of high-quality data. By training a language model on this dataset, we can convert web data with irregular formats into high-quality ones. Our experiments show that training with the model-transformed data yields better results, surpassing training with only high-quality data by an average score of 9.4% in Chinese math problems. Additionally, our 7B model outperforms several open-source models larger than 32B and surpasses well-known closed-source models such as GPT-3.5, highlighting the efficacy of our approach. We have released our code at https://github.com/zhouj8553/Web_to_SFT.- Anthology ID:
- 2024.findings-emnlp.660
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2024
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 11297–11312
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2024.findings-emnlp.660/
- DOI:
- 10.18653/v1/2024.findings-emnlp.660
- Cite (ACL):
- Jing Zhou, Chenglin Jiang, Wei Shen, Xiao Zhou, and Xiaonan He. 2024. Leveraging Web-Crawled Data for High-Quality Fine-Tuning. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 11297–11312, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- Leveraging Web-Crawled Data for High-Quality Fine-Tuning (Zhou et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2024.findings-emnlp.660.pdf