mlsanityData to 7 Oct 2026

Lightweight Multimodal LLM-Enabled Cost-Effective Defect Grading of Power Transmission Equipment

arXiv 2605.28822 ↗First posted 25 Mar 2026cs.CLcited by 0

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Abstract (arXiv)

Defect grading of power transmission equipment (DGPTE) is crucial to the stability of electric energy transmission. Although existing machine learning methods exhibit strong capabilities in defect detection, they are plagued by difficulties in integrating expert experience and facing class imbalance in more refined defect grading field. To address this issue, this paper introduces a novel defect grading framework based on multimodal large language model (MLLM). Specifically, this approach maximizes the commercial MLLMs' potential of DGPTE through in-context learning and obtains the state-of-te-art (SOTA) model. By sending a secondary request to this model, a small number of chain of thought-based question-answer pairs (Q&As) are generated, which effectively reduces the cost of manual annotation. In this way, these high-quality interpretable Q&As are used to train Qwen3-VL-8B via Low-Rank Adaption-based supervised fine-tuning (SFT). Experimental results on three DGPTE tasks demonstrate that fine-tuning only the language model layer yields the SOTA performance. Furthermore, multi-task joint fine-tuning verifies the feasibility of handling multiple grading tasks within only a single lightweight MLLM.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 35 references that are in the map. First posted on arXiv 25 Mar 2026.

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References · 6

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  3. 2021True Few-Shot Learning with Language Modelscited by 343
  4. 2026Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Rankingcited by 211
  5. 2025Solar Photovoltaic Assessment with Large Language Modelcited by 7
  6. 2025IAD-GPT: Advancing Visual Knowledge in Multimodal Large Language Model for Industrial Anomaly Detectioncited by 2

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  3. 2025Towards Open-Vocabulary Industrial Defect Understanding with a Large-Scale Multimodal Dataset
  4. 2025Intelligent Power Grid Design Review via Active Perception-Enabled Multimodal Large Language Models
  5. 2024SubstationAI: Multimodal Large Model-Based Approaches for Analyzing Substation Equipment Faults
  6. 2024FabGPT: An Efficient Large Multimodal Model for Complex Wafer Defect Knowledge Queries
  7. 2024VSLLaVA: a pipeline of large multimodal foundation model for industrial vibration signal analysis
  8. 2025LLM-YOLOMS: Large Language Model-based Semantic Interpretation and Fault Diagnosis for Wind Turbine Components