Summary of Empowering Large Language Models For Textual Data Augmentation, by Yichuan Li et al.
Empowering Large Language Models for Textual Data Augmentation
by Yichuan Li, Kaize Ding, Jianling Wang, Kyumin Lee
First submitted to arxiv on: 26 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel approach for generating high-quality textual data using Large Language Models (LLMs) is proposed. The quality of augmented data relies heavily on the instructions provided, which can be task-dependent and require manual crafting. To address scalability and consistency issues, an automatic method is developed to generate a large pool of augmentation instructions and select the most suitable ones for each downstream task. Experimental results show that this approach consistently produces better-quality augmented data than non-LLM and LLM-based methods, leading to improved performance on 26 few-shot learning tasks across various application domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can be used to create new text by following instructions. This works well if the instructions are good, but making them is hard because they depend on what task you’re trying to solve. To fix this problem, a new way is developed to automatically generate lots of instructions and choose the best ones for each task. This helps LLMs make better text augmentation, which leads to better results in many different tasks. |
Keywords
» Artificial intelligence » Few shot
