Summary of Leveraging Large Language Models For Structure Learning in Prompted Weak Supervision, by Jinyan Su et al.
Leveraging Large Language Models for Structure Learning in Prompted Weak Supervision
by Jinyan Su, Peilin Yu, Jieyu Zhang, Stephen H. Bach
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to weak supervision, which involves using pre-trained large language models (LLMs) as the basis for labeling functions (LFs). The authors extend the use of LLMs in the loop to learn the statistical dependency structure among supervision sources. They introduce the Structure Refining Module, which consists of Labeling Function Removal (LaRe) and Correlation Structure Generation (CosGen), to capture intrinsic structures in the embedding space. Compared to previous methods that rely on weak labels, this approach finds dependencies less dependent on data. The authors demonstrate improved performance by up to 12.7 points on benchmark tasks. They also explore efficiency-performance trade-offs through comprehensive ablation experiments and analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses big language models to help computers learn from limited labeled data. It’s like a game where the model answers questions based on what it already knows, and then uses those answers to learn more. The goal is to improve how well computers can learn from small amounts of data by using these models in new ways. The authors came up with a special module that helps the model understand relationships between different sources of information. This led to some big improvements – up to 12.7 points better! They also looked at how this approach balances speed and accuracy, which is important when working with large amounts of data. |
Keywords
* Artificial intelligence * Embedding space
