Explanova: Automatically Discover Data Insights in N \times M Table via XAI Combined LLM Workflow
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arXiv
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866912831619727360 |
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| author | Huang, Yiming |
| author_facet | Huang, Yiming |
| contents | Automation in data analysis has been a long-time pursuit. Current agentic LLM shows a promising solution towards it. Like DeepAnalyze, DataSage, and Datawise. They are all powerful agentic frameworks for automatic fine-grained analysis and are powered by LLM-based agentic tool calling ability. However, what about powered by a preset AutoML-like workflow? If we traverse all possible exploration, like Xn itself`s statistics, Xn1-Xn2 relationships, Xn to all other, and finally explain? Our Explanova is such an attempt: Cheaper due to a Local Small LLM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_12317 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Explanova: Automatically Discover Data Insights in N \times M Table via XAI Combined LLM Workflow Huang, Yiming Machine Learning Artificial Intelligence Automation in data analysis has been a long-time pursuit. Current agentic LLM shows a promising solution towards it. Like DeepAnalyze, DataSage, and Datawise. They are all powerful agentic frameworks for automatic fine-grained analysis and are powered by LLM-based agentic tool calling ability. However, what about powered by a preset AutoML-like workflow? If we traverse all possible exploration, like Xn itself`s statistics, Xn1-Xn2 relationships, Xn to all other, and finally explain? Our Explanova is such an attempt: Cheaper due to a Local Small LLM. |
| title | Explanova: Automatically Discover Data Insights in N \times M Table via XAI Combined LLM Workflow |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2601.12317 |