From Data to Insights: Exploring Program-of-Thoughts Prompting for Chart Summarization
Quick Take
This paper introduces a novel approach to chart summarization using zero-shot learning with lightweight visual language models (VLMs). By employing Python programs for computational reasoning, the proposed method achieves comparable performance to existing techniques while enhancing flexibility through a chart-to-dictionary auxiliary task. The results indicate effectiveness across semantic and factual metrics, with code available for further exploration.
Key Points
- Introduces a chart-to-dictionary auxiliary task for enhanced flexibility.
- Employs zero-shot learning to motivate lightweight VLMs for computational reasoning.
- Achieves performance on par with existing chart summarization methods.
- Focuses on improving semantic visual understanding and numerical reasoning.
- Code available at https://anonymous.4open.science/r/ZeroShot-PoT-C2T-5A6B.
Article Content
From source RSS / original summaryarXiv:2605. 28874v1 Announce Type: new Abstract: Charts play a critical role in conveying numerical data insights through structured visual representations. However, semantic visual understanding and numerical reasoning requirements hinder the accurate description of charts, interpreting a challenging task in chart summarization. Despite recent advancements in visual language models (VLMs), approaches lack robust mechanisms for verifying statistical fact correctness and are computationally heavy.
To address this gap, this paper explores a strategy of using zero-shot learning to motivate the lightweight VLMs to perform computational reasoning, via Python programs as intermediaries to derive valid summary statistics for chart understanding. Specifically, we introduce a novel chart-to-dictionary auxiliary task, offering a more flexible representation compared to traditional chart-to-table methods, making it particularly well-suited for integration with the Program-of-Thought (PoT) strategy.
Experimental results demonstrate our strategy performs on par with existing chart summarization methods across semantic and factual metrics. Code is available on https://anonymous. 4open. science/r/ZeroShot-PoT-C2T-5A6B.
Reader Mode unavailable (could not extract clean content).
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.CL
See more →Time to REFLECT: Can We Trust LLM Judges for Evidence-based Research Agents?
The reliability of LLM judges for evaluating deep research agents is critically assessed using the REFLECT benchmark.