Summary of Enhancing Text Annotation Through Rationale-driven Collaborative Few-shot Prompting, by Jianfei Wu and Xubin Wang and Weijia Jia
Enhancing Text Annotation through Rationale-Driven Collaborative Few-Shot Prompting
by Jianfei Wu, Xubin Wang, Weijia Jia
First submitted to arxiv on: 15 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this study, researchers investigate the potential of large language models (LLMs) as automated data annotators to improve efficiency and consistency in annotation tasks. The team employs rationale-driven collaborative few-shot prompting techniques to enhance LLM performance in text annotation. A rigorous evaluation is conducted across six LLMs, four benchmark datasets, and seven distinct methodologies. Results show that collaborative methods outperform traditional techniques, especially in complex annotation tasks. This work provides valuable insights and a robust framework for tackling challenging text annotation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Automated data annotators could make it easier and more accurate to label large amounts of data. Researchers are exploring whether big language models can help with this task. They’re using special techniques that involve giving the models hints, or “prompts,” to see if they can do a better job than usual. The study looks at six different language models and four sets of test data to see which approach works best. |
Keywords
» Artificial intelligence » Few shot » Prompting
