Dataset Thinning for faster fine-tuning of LLMs

The text discusses the importance of dataset quality in fine-tuning large language models (LLMs) and how it can be improved using dataset thinning techniques. Dataset thinning involves removing redundant data points to reduce the computational load and improve training efficiency. The article proposes a method for clustering datasets using DBSCAN, which identifies noise points and clusters, allowing for the removal of redundant data. The proposed method is demonstrated with an example dataset, where most of the data points were identified as noise, and 50% of the non-noise clusters were randomly reduced. The results show that fine-tuning on the thinned dataset leads to better performance compared to fine-tuning on the full dataset, and the model outperforms a base model in benchmarking. The article concludes by highlighting the potential benefits of using clustering as a metric to understand dataset quality and reduce dataset size.

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