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Wuhan University of Technology

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Research library247linked papers
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Selected work

Representative Papers

A Comprehensive Evaluation of Parameter-Efficient Fine-Tuning on Method-Level Code Smell Detection

Dec 18, 2024arXiv.org

Existing code smell detection approaches—ranging from heuristic rules to machine learning (ML) and deep learning (DL)—exhibit limited performance, while full fine-tuning of large language models (LLMs) incurs prohibitive computational costs. This work presents the first systematic evaluation of parameter-efficient fine-tuning (PEFT) techniques—including Prompt Tuning, Prefix Tuning, LoRA, and (IA)³—for method-level code bad smells (e.g., Complex Conditional, Long Method) detection. We conduct experiments across four small language models and six LLMs using a high-quality GitHub dataset. Key findings: (1) Small models combined with PEFT significantly outperform both large models with PEFT and even fully fine-tuned LLMs; (2) Training data scale exerts a far greater impact on detection accuracy than the number of tunable parameters; (3) PEFT achieves comparable or superior accuracy to full fine-tuning while drastically reducing GPU memory consumption and consistently surpassing traditional detectors. Our study establishes a novel, lightweight, efficient, and deployable paradigm for code quality analysis.

2 citationsRead paper

SDR-CIR: Semantic Debias Retrieval Framework for Training-Free Zero-Shot Composed Image Retrieval

Feb 04, 2026

This work addresses the semantic bias introduced by vague textual descriptions generated by multimodal large language models in zero-shot compositional image retrieval, which often degrades retrieval accuracy. To mitigate this issue, the authors propose a training-free semantic debiasing reranking framework that leverages a selective chain-of-thought prompting strategy to guide the model toward salient visual content. The framework incorporates a two-stage mechanism: an anchoring stage that enriches missing semantic cues and a debiasing stage that explicitly corrects description bias by suppressing redundant information through a penalty term. This approach represents the first explicit semantic debiasing mechanism tailored for zero-shot compositional image retrieval, achieving state-of-the-art performance among single-stage methods on three standard CIR benchmarks while maintaining both efficiency and accuracy.

1 citationsRead paper

Generative AI-enhanced Probabilistic Multi-Fidelity Surrogate Modeling Via Transfer Learning

Jan 20, 2026arXiv.org

This work addresses the challenge that high-fidelity data are scarce and costly, while abundant low-fidelity data lack sufficient accuracy, thereby limiting surrogate model performance. To overcome this, the authors propose a probabilistic multi-fidelity surrogate framework that integrates transfer learning with generative modeling. Built upon a normalizing flow architecture incorporating surjective layers, the model is first pre-trained on extensive low-fidelity data and then fine-tuned with only a small amount of high-fidelity data, enabling efficient knowledge transfer and uncertainty quantification. This approach transcends the dimensional constraints of conventional bijective flows by supporting learnable dimensionality reduction while preserving exact likelihood-based training, marking the first deep integration of generative AI into multi-fidelity modeling. Validated on ballasted railway sleeper and reinforced concrete slab systems, the method achieves highly accurate probabilistic predictions using minimal high-fidelity simulations, significantly outperforming low-fidelity-only baselines.

1 citationsRead paper
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