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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/2412.06303 |
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| _version_ | 1866916618781589504 |
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| author | Cho, Hyowon Ka, Soonwon Park, Daechul Kang, Jaewook Seo, Minjoon Son, Bokyung |
| author_facet | Cho, Hyowon Ka, Soonwon Park, Daechul Kang, Jaewook Seo, Minjoon Son, Bokyung |
| contents | Large language models (LLMs) often struggle to objectively identify latent characteristics in large datasets due to their reliance on pre-trained knowledge rather than actual data patterns. To address this data grounding issue, we propose Data Scientist AI (DSAI), a framework that enables unbiased and interpretable feature extraction through a multi-stage pipeline with quantifiable prominence metrics for evaluating extracted features. On synthetic datasets with known ground-truth features, DSAI demonstrates high recall in identifying expert-defined features while faithfully reflecting the underlying data. Applications on real-world datasets illustrate the framework's practical utility in uncovering meaningful patterns with minimal expert oversight, supporting use cases such as interpretable classification.
The title of our paper is chosen from multiple candidates based on DSAI-generated criteria. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_06303 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DSAI: Unbiased and Interpretable Latent Feature Extraction for Data-Centric AI Cho, Hyowon Ka, Soonwon Park, Daechul Kang, Jaewook Seo, Minjoon Son, Bokyung Machine Learning Artificial Intelligence Large language models (LLMs) often struggle to objectively identify latent characteristics in large datasets due to their reliance on pre-trained knowledge rather than actual data patterns. To address this data grounding issue, we propose Data Scientist AI (DSAI), a framework that enables unbiased and interpretable feature extraction through a multi-stage pipeline with quantifiable prominence metrics for evaluating extracted features. On synthetic datasets with known ground-truth features, DSAI demonstrates high recall in identifying expert-defined features while faithfully reflecting the underlying data. Applications on real-world datasets illustrate the framework's practical utility in uncovering meaningful patterns with minimal expert oversight, supporting use cases such as interpretable classification. The title of our paper is chosen from multiple candidates based on DSAI-generated criteria. |
| title | DSAI: Unbiased and Interpretable Latent Feature Extraction for Data-Centric AI |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2412.06303 |