Language-Specific Token Pruning Transforms Code Summarization Efficiency

Training large language models to summarize code is resource-intensive, and performance often degrades when processing longer inputs. A new study from arXiv tackles this challenge by exploring how selective token removal can reduce computational costs without sacrificing the quality of generated summaries.

The Core Finding#

The research reveals a counterintuitive result: the tokens you keep matter far more than how many you remove. Rather than a one-size-fits-all approach, the team found that Java and Python—despite both being popular programming languages—benefit from entirely different token-reduction strategies. This suggests that language-aware curation is essential for optimizing code summarization systems.

Three Reduction Strategies Tested#

The researchers evaluated three distinct techniques for trimming token overhead:

• Abstract Syntax Tree (AST) representations — extracting the structural skeleton of code
• Function Signatures — keeping only method/function declarations
• CrystalBLEU-guided pruning — leveraging semantic evaluation to identify which tokens matter most

Each approach was combined with semantic filtering and tested in both isolated and cascaded reduction modes.

Language-Dependent Results#

Performance diverged sharply across languages. In Java, AST optimization delivered a 37% improvement in performance while cutting 56–73% of tokens. The same technique, however, backfired in Python, causing up to 49% degradation. Conversely, Function Signatures performed poorly for Java but excelled in Python, achieving 83% token reduction while preserving quality. CrystalBLEU proved more reliable across both languages, delivering 60–72% reduction with consistent results.

Implications#

The findings challenge the assumption that optimization techniques transfer across languages. Effective code summarization for LLMs demands language-specific strategies rather than generic token-cutting rules, reshaping how engineers should approach efficiency gains in automated documentation systems.