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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.12726 |
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| _version_ | 1866929471287721984 |
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| author | Lee, Christopher J. Tran, Giorgio Tabalba, Roderick Leigh, Jason Longman, Ryan |
| author_facet | Lee, Christopher J. Tran, Giorgio Tabalba, Roderick Leigh, Jason Longman, Ryan |
| contents | This paper explores the intersection of data visualization and Large Language Models (LLMs). Driven by the need to make a broader range of data visualization types accessible for novice users, we present a guided LLM-based pipeline designed to transform data, guided by high-level user questions (referred to as macro-queries), into a diverse set of useful visualizations. This approach leverages various prompting techniques, fine-tuning inspired by Abela's Chart Taxonomy, and integrated SQL tool usage. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_12726 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Macro-Queries: An Exploration into Guided Chart Generation from High Level Prompts Lee, Christopher J. Tran, Giorgio Tabalba, Roderick Leigh, Jason Longman, Ryan Computation and Language This paper explores the intersection of data visualization and Large Language Models (LLMs). Driven by the need to make a broader range of data visualization types accessible for novice users, we present a guided LLM-based pipeline designed to transform data, guided by high-level user questions (referred to as macro-queries), into a diverse set of useful visualizations. This approach leverages various prompting techniques, fine-tuning inspired by Abela's Chart Taxonomy, and integrated SQL tool usage. |
| title | Macro-Queries: An Exploration into Guided Chart Generation from High Level Prompts |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2408.12726 |