LLM-assisted Labeling Function Generation for Semantic Type Detection
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Prompt EngineeringSimilar Papers 제목 키워드 기반
AHA: Human-Assisted Out-of-Distribution Generalization and Detection
Modern machine learning models deployed often encounter distribution shifts in real-world applications, manifesting as covariate or semantic out-of-distribution (OOD) shifts. These shifts give rise to challenges in OOD g…
Out-of-Distribution GeneralizationVeriStruct: AI-assisted Automated Verification of Data-Structure Modules in Verus
We introduce VeriStruct, a novel framework that extends AI-assisted automated verification from single functions to more complex data structure modules in Verus. VeriStruct employs a planner module to orchestrate the sys…
TagLab: A human-centric AI system for interactive semantic segmentation
Fully automatic semantic segmentation of highly specific semantic classes and complex shapes may not meet the accuracy standards demanded by scientists. In such cases, human-centered AI solutions, able to assist operator…
SegmentationSemantic SegmentationAnalyzing the Cross-Sensor Portability of Neural Network Architectures for LiDAR-based Semantic Labeling
State-of-the-art approaches for the semantic labeling of LiDAR point clouds heavily rely on the use of deep Convolutional Neural Networks (CNNs). However, transferring network architectures across different LiDAR sensor …
TypeDance: Creating Semantic Typographic Logos from Image through Personalized Generation
Semantic typographic logos harmoniously blend typeface and imagery to represent semantic concepts while maintaining legibility. Conventional methods using spatial composition and shape substitution are hindered by the co…