InsightEval: An Expert-Curated Benchmark for Assessing Insight Discovery in LLM-Driven Data Agents
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913169161584640 |
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| author | Zhu, Zhenghao Song, Yuanfeng Chen, Xin Liu, Chengzhong Cui, Yakun Cao, Caleb Chen Han, Sirui Guo, Yike |
| author_facet | Zhu, Zhenghao Song, Yuanfeng Chen, Xin Liu, Chengzhong Cui, Yakun Cao, Caleb Chen Han, Sirui Guo, Yike |
| contents | Data analysis has become an indispensable part of scientific research. To discover the latent knowledge and insights hidden within massive datasets, we need to perform deep exploratory analysis to realize their full value. With the advent of large language models (LLMs) and multi-agent systems, more and more researchers are making use of these technologies for insight discovery. However, there are few benchmarks for evaluating insight discovery capabilities. As one of the most comprehensive existing frameworks, InsightBench also suffers from many critical flaws: format inconsistencies, poorly conceived objectives, and redundant insights. These issues may significantly affect the quality of data and the evaluation of agents. To address these issues, we thoroughly investigate shortcomings in InsightBench and propose essential criteria for a high-quality insight benchmark. Regarding this, we develop a data-curation pipeline to construct a new dataset named InsightEval. We further introduce a novel metric to measure the exploratory performance of agents. Through extensive experiments on InsightEval, we highlight prevailing challenges in automated insight discovery and raise some key findings to guide future research in this promising direction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_22884 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | InsightEval: An Expert-Curated Benchmark for Assessing Insight Discovery in LLM-Driven Data Agents Zhu, Zhenghao Song, Yuanfeng Chen, Xin Liu, Chengzhong Cui, Yakun Cao, Caleb Chen Han, Sirui Guo, Yike Artificial Intelligence Data analysis has become an indispensable part of scientific research. To discover the latent knowledge and insights hidden within massive datasets, we need to perform deep exploratory analysis to realize their full value. With the advent of large language models (LLMs) and multi-agent systems, more and more researchers are making use of these technologies for insight discovery. However, there are few benchmarks for evaluating insight discovery capabilities. As one of the most comprehensive existing frameworks, InsightBench also suffers from many critical flaws: format inconsistencies, poorly conceived objectives, and redundant insights. These issues may significantly affect the quality of data and the evaluation of agents. To address these issues, we thoroughly investigate shortcomings in InsightBench and propose essential criteria for a high-quality insight benchmark. Regarding this, we develop a data-curation pipeline to construct a new dataset named InsightEval. We further introduce a novel metric to measure the exploratory performance of agents. Through extensive experiments on InsightEval, we highlight prevailing challenges in automated insight discovery and raise some key findings to guide future research in this promising direction. |
| title | InsightEval: An Expert-Curated Benchmark for Assessing Insight Discovery in LLM-Driven Data Agents |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2511.22884 |