  In this paper, we propose a solution for improving the quality of captions
generated for figures in papers. We adopt the approach of summarizing the
textual content in the paper to generate image captions. Throughout our study,
we encounter discrepancies in the OCR information provided in the official
dataset. To rectify this, we employ the PaddleOCR toolkit to extract OCR
information from all images. Moreover, we observe that certain textual content
in the official paper pertains to images that are not relevant for captioning,
thereby introducing noise during caption generation. To mitigate this issue, we
leverage LLaMA to extract image-specific information by querying the textual
content based on image mentions, effectively filtering out extraneous
information. Additionally, we recognize a discrepancy between the primary use
of maximum likelihood estimation during text generation and the evaluation
metrics such as ROUGE employed to assess the quality of generated captions. To
bridge this gap, we integrate the BRIO model framework, enabling a more
coherent alignment between the generation and evaluation processes. Our
approach ranked first in the final test with a score of 4.49.
