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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.28874 |
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| _version_ | 1866914609567367168 |
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| author | Qu, Yutong Zhang, Wei |
| author_facet | Qu, Yutong Zhang, Wei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_28874 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | From Data to Insights: Exploring Program-of-Thoughts Prompting for Chart Summarization Qu, Yutong Zhang, Wei Computation and Language 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. |
| title | From Data to Insights: Exploring Program-of-Thoughts Prompting for Chart Summarization |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2605.28874 |