Summary of Text2chart31: Instruction Tuning For Chart Generation with Automatic Feedback, by Fatemeh Pesaran Zadeh et al.
Text2Chart31: Instruction Tuning for Chart Generation with Automatic Feedback
by Fatemeh Pesaran Zadeh, Juyeon Kim, Jin-Hwa Kim, Gunhee Kim
First submitted to arxiv on: 5 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A hierarchical pipeline is proposed to address challenges in visualizing complex, real-world data through charts and plots. Large language models (LLMs) have demonstrated strong capabilities across various language tasks, but existing datasets rarely cover a full range of chart types. To overcome this limitation, the authors introduce Text2Chart31, a new dataset containing 31 unique plot types referring to the Matplotlib library, along with descriptions, code, data tables, and plots. Additionally, a reinforcement learning-based instruction tuning technique is developed for chart generation tasks without requiring human feedback. Experimental results show that this approach significantly enhances model performance, enabling smaller models to outperform larger open-source models and be comparable to state-of-the-art proprietary models in data visualization tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of making charts using artificial intelligence has been developed. The current methods for creating charts are limited because the datasets used rarely include all types of charts. To fix this, a new dataset called Text2Chart31 has been created. This dataset contains many different types of charts and is linked to the Matplotlib library. The authors also introduced a new way of teaching AI models to create charts without needing human help. They tested their approach and found that it improved the performance of smaller AI models compared to larger ones, making them just as good as more advanced proprietary models for creating charts. |
Keywords
» Artificial intelligence » Instruction tuning » Reinforcement learning
