ChartThinker introduces a novel approach to chart summarization by leveraging context retrieval and chain of thought. It addresses deficiencies in existing methods by improving logical coherence and accuracy in generated summaries. Extensive empirical analysis demonstrates superior performance over state-of-the-art models across various evaluation metrics. The model integrates thought chains with context retrieval for enhanced reasoning ability.
To Another Language
from source content
arxiv.org
Key Insights Distilled From
by Mengsha Liu,... at arxiv.org 03-19-2024
https://arxiv.org/pdf/2403.11236.pdfDeeper Inquiries