A data synthesis framework generates four syntax/semantics code pair types, and fine-tuning embedding models on the resulting datasets improves code clone detection, functional consistency, and retrieval.
AutoAugment Is What You Need: Enhancing Rule-based Augmentation Methods in Low-resource Regimes
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abstract
Text data augmentation is a complex problem due to the discrete nature of sentences. Although rule-based augmentation methods are widely adopted in real-world applications because of their simplicity, they suffer from potential semantic damage. Previous researchers have suggested easy data augmentation with soft labels (softEDA), employing label smoothing to mitigate this problem. However, finding the best factor for each model and dataset is challenging; therefore, using softEDA in real-world applications is still difficult. In this paper, we propose adapting AutoAugment to solve this problem. The experimental results suggest that the proposed method can boost existing augmentation methods and that rule-based methods can enhance cutting-edge pre-trained language models. We offer the source code.
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Functional Consistency of LLM Code Embeddings: A Self-Evolving Data Synthesis Framework for Benchmarking
A data synthesis framework generates four syntax/semantics code pair types, and fine-tuning embedding models on the resulting datasets improves code clone detection, functional consistency, and retrieval.