arXiv:2611.00242v1 Announce Type: new Abstract: A deep understanding of scientific literature often relies on interpreting figures that contain key information beyond the main text. Leveraging Large Language Models (LLMs) to extract insights from these figures can significantly accelerate knowledge discovery. Existing works on Scientific Visual Question Answering rely on resource-intensive training pipelines that are often inaccessible and require large computational resources. Instead of designing complex architectures, we propose RE- SciVQA, which focuses on improving the quality of retrieved context and fine-tuning small LLMs with parameter-efficient LoRA training. Using a simple query expansion step, relevant section headers are appended to the original question to improve the retrieval coverage. In addition, a simple adjustment of similarity scores is applied using figure references and caption alignment. On the SciVQA benchmark, RE-SciVQA achieves a ROUGE-1 F1 score of 73.9%, outperforming chart-focused models such as UniChart (14.7%), ChartGemma (6.9%), and Matcha-ChartQA (6.3%). Our relevance-enhanced RAG setup also improves the exact match by 11.9% and the fuzzy match by 29.5% compared with a standard RAG baseline, resulting in an overall gain of 14.3%. We also examine closed-ended infinite visual questions, a category in which chart-understanding models often struggle. Our experiments show that using plot digitization instead of synthetic captions leads to an 18.5% improvement for this question type.
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