An Experimental Study on the Text Generation Capability for Chart Image Descriptions in Korean SLLM

Hyojun An, Sungpil Choi

http://doi.org/10.5626/JOK.2025.52.2.132

This study explores the capability of using Small Large Language Models(SLLMs) for automatically generating and interpreting information from chart images. To achieve this goal, we built an instruction dataset for SLLM training by extracting text data from chart images and adding descriptive information. We conducted instruction tuning on a Korean SLLM and evaluated its ability to generate information from chart images. The experimental results demonstrated that the SLLM, which was fine-tuned with the constructed instruction dataset, was capable of generating descriptive text comparable to OpenAI's GPT-4o-mini API. This study suggests that, in the future, Korean SLLMs may be effectively used for generating descriptive text and providing information across a broader range of visual data.