Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.16173 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914928426745856 |
|---|---|
| author | Li, Chenjie Zhang, Dan Wang, Jin |
| author_facet | Li, Chenjie Zhang, Dan Wang, Jin |
| contents | Detecting semantic types of columns in data lake tables is an important application. A key bottleneck in semantic type detection is the availability of human annotation due to the inherent complexity of data lakes. In this paper, we propose using programmatic weak supervision to assist in annotating the training data for semantic type detection by leveraging labeling functions. One challenge in this process is the difficulty of manually writing labeling functions due to the large volume and low quality of the data lake table datasets. To address this issue, we explore employing Large Language Models (LLMs) for labeling function generation and introduce several prompt engineering strategies for this purpose. We conduct experiments on real-world web table datasets. Based on the initial results, we perform extensive analysis and provide empirical insights and future directions for researchers in this field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16173 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LLM-assisted Labeling Function Generation for Semantic Type Detection Li, Chenjie Zhang, Dan Wang, Jin Databases Artificial Intelligence Detecting semantic types of columns in data lake tables is an important application. A key bottleneck in semantic type detection is the availability of human annotation due to the inherent complexity of data lakes. In this paper, we propose using programmatic weak supervision to assist in annotating the training data for semantic type detection by leveraging labeling functions. One challenge in this process is the difficulty of manually writing labeling functions due to the large volume and low quality of the data lake table datasets. To address this issue, we explore employing Large Language Models (LLMs) for labeling function generation and introduce several prompt engineering strategies for this purpose. We conduct experiments on real-world web table datasets. Based on the initial results, we perform extensive analysis and provide empirical insights and future directions for researchers in this field. |
| title | LLM-assisted Labeling Function Generation for Semantic Type Detection |
| topic | Databases Artificial Intelligence |
| url | https://arxiv.org/abs/2408.16173 |