LoRA-MME: Multi-Model Ensemble of LoRA-Tuned Encoders for Code Comment Classification
Code comment classification is a critical task for automated software documentation and analysis. In the context of the NLBSE'26 Tool Competition, we present LoRA-MME, a Multi-Model Ensemble architecture utilizing Parameter-Efficient Fine-Tuning (PEFT). Our approach addresses the multi-label classification challenge across Java, Python, and Pharo by combining the strengths of four distinct transformer encoders: UniXcoder, CodeBERT, GraphCodeBERT, and CodeBERTa. By independently fine-tuning these models using Low-Rank Adaptation(LoRA) and aggregating their predictions via a learned weighted ensemble strategy, we maximize classification performance without the memory overhead of full model ...
| Authors | Md Akib Haider, Ahsan Bulbul, Nafis Fuad Shahid et al. |
|---|---|
| Primary category | cs.SE |
| Published | 4 March 2026 |
| Last updated at source | 15 April 2026 |
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