LoRA-MME: Multi-Model Ensemble of LoRA-Tuned Encoders for Code Comment Classification

TL;DR

LoRA-MME introduces a multi-model ensemble of fine-tuned transformer encoders for multi-label code comment classification, achieving high accuracy with efficient parameter tuning.

Contribution

It presents a novel ensemble approach using LoRA for multi-model fine-tuning across multiple programming languages, enhancing classification performance.

Findings

Achieved an F1 Weighted score of 0.7906

Ensemble method improved classification accuracy

Trade-off observed between accuracy and inference efficiency

Abstract

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 fine-tuning. Our tool achieved an F1 Weighted score of 0.7906 and a Macro F1 of 0.6867 on the test set. However, the computational cost of the ensemble resulted in a final submission score of 41.20%, highlighting the trade-off between semantic accuracy and inference efficiency.