CodeBPE: Investigating Subtokenization Options for Large Language Model Pretraining on Source Code

Recent works has widely adopted large language model pretraining for source code, suggested source code-specific pretraining objectives and investigated the applicability of various Transformer-based language model architectures for source code. This work investigates another important aspect of such models, the effect of different subtokenization options, and aims at identifying most effective and length-efficient subtokenizations, taking into account source code specifics. We propose subtokenziation that reduces average length by 17--40% without downstream performance drop, and show that a carefully chosen subtokenization may significantly improve quality by 0.5-2%, possibly with some length increase.

[Note: This contribution also has a spotlight talk. Please find the paper here: https://openreview.net/forum?id=rd-G1nO-Jbq]