本技术报告探讨了通过微调本地视觉语言模型,实现汉字硬笔书写质量自动评价的技术方案。针对传统评价方法难以提供准确性反馈的问题,我们团队采用精心设计的prompt并结合微调的方式构建了一个高效的汉字硬笔书写质量自动评价系统。我们采用Qwen2.5-VL-7B-Instruct模型作为基础,通过LoRA微调技术实现了汉字书写质量等级分类(子任务一)和个性化评语生成(子任务二)的功能。系统地融合了视觉特征分析与语言生成能力,在训练过程中采用了梯度检查点、BF16混合精度训练等技术优化显存使用,并设计了针对性的损失函数和评估指标。实验结果表明,我们的方法能够有效实现汉字书写质量的细粒度评价。

“本技术报告详细介绍了我们团队在第五届空间语义理解评测(SpaCE2025)中的方法与成果。SpaCE2025 继续聚焦大语言模型在空间语义理解方面的能力评估,涵盖空间语言理解与空间推理两个核心维度,共设置五个子任务:空间信息正误判断、空间参照实体判断、空间异形同义判断、中文空间方位关系推理以及英文空间方位关系推理。我们通过设计结构化提示词并引入思维链推理机制,结合LoRA 微调技术和投票集成方法,有效提升了大语言模型在空间语义理解任务中的表现。在最终评测中,我们团队五个子任务的综合准确率为0.5983,整体排名第五。”

Inspired by the superior language abilities of large language models (LLM), large vision-language models (LVLM) have been recently proposed by integrating powerful LLMs for improving the performance on complex multimodal tasks. Despite the promising progress on LVLMs, we find that they suffer from object hallucinations, i.e., they tend to generate objects inconsistent with the target images in the descriptions. To investigate it, this work presents the first systematic study on object hallucination of LVLMs. We conduct the evaluation experiments on several representative LVLMs, and show that they mostly suffer from severe object hallucination issues. We further discuss that the visual instructions may influence the hallucination, and find that: objects that frequently appear in the visual instructions or co-occur with the image objects are obviously prone to be hallucinated by LVLMs. Besides, we further design a polling-based query method called POPE for better evaluation of object hallucination. Experiment results show that our POPE can evaluate object hallucination in a more stable and flexible way.