Decoding Data Quality via Synthetic Corruptions: Embedding-guided
  Pruning of Code Data
Yu Yang Aaditya K. Singh Mostafa Elhoushi Anas Mahmoud Kushal Tirumala Fabian Gloeckle Baptiste Rozière Carole-Jean Wu Ari S. Morcos Newsha Ardalani
Code datasets, often collected from diverse and uncontrolled sources such as
GitHub, potentially suffer from quality issues, thereby affecting the
performance and training efficiency of Large Language Models (LLMs) optimized
for code generation. Previous studies demonstrated the benefit of using
embedding spaces for data pruning, but they mainly focused on duplicate removal
or increasing variety, and in other modalities, such as images. Our work
focuses on using embeddings to identify and remove "low-quality" code data.
First, we explore features of "low-quality" code in embedding space, through
the use of synthetic corruptions. Armed with this knowledge, we devise novel
pruning metrics that operate in embedding space to identify and remove
low-quality entries in the Stack dataset. We demonstrate the benefits of this
synthetic corruption informed pruning (SCIP) approach on the well-established
HumanEval and MBPP benchmarks, outperforming existing embedding-based methods.
Importantly, we achieve up to a 3% performance improvement over no pruning,
thereby showing the promise of insights from synthetic corruptions for data
pruning.