Isotropy Matters: Soft-ZCA Whitening of Embeddings for Semantic Code Search

• URL: http://arxiv.org/abs/2411.17538v2
• Date: Wed, 27 Nov 2024 09:43:01 GMT
• Title: Isotropy Matters: Soft-ZCA Whitening of Embeddings for Semantic Code Search
• Authors: Andor Diera, Lukas Galke, Ansgar Scherp,
• Abstract summary: Low isotropy in an embedding space impairs performance on tasks involving semantic inference. We propose a modified ZCA whitening technique to control isotropy levels in embeddings.
• Score: 6.704529554100875
• License:
• Abstract: Low isotropy in an embedding space impairs performance on tasks involving semantic inference. Our study investigates the impact of isotropy on semantic code search performance and explores post-processing techniques to mitigate this issue. We analyze various code language models, examine isotropy in their embedding spaces, and its influence on search effectiveness. We propose a modified ZCA whitening technique to control isotropy levels in embeddings. Our results demonstrate that Soft-ZCA whitening improves the performance of pre-trained code language models and can complement contrastive fine-tuning.

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