Summary of Chain Of Stance: Stance Detection with Large Language Models, by Junxia Ma et al.
Chain of Stance: Stance Detection with Large Language Models
by Junxia Ma, Changjiang Wang, Hanwen Xing, Dongming Zhao, Yazhou Zhang
First submitted to arxiv on: 3 Aug 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 The paper proposes a new approach to stance detection using large language models (LLMs) as expert detectors. Unlike existing methods, which focus on fine-tuning LLMs with large datasets, this method, called Chain of Stance (CoS), decomposes the stance detection process into intermediate assertions that culminate in the final judgment. This approach leads to significant improvements in classification performance, achieving state-of-the-art results with an F1 score of 79.84 in the few-shot setting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses big language models to figure out what people are saying about certain things. It’s like trying to understand what someone is thinking or feeling when they write something. The model breaks down the task into smaller steps, like asking if someone likes or dislikes something, and then puts it all together to make a final judgment. This makes the model really good at understanding what people mean. |
Keywords
» Artificial intelligence » Classification » F1 score » Few shot » Fine tuning
