Summary of Sqbc: Active Learning Using Llm-generated Synthetic Data For Stance Detection in Online Political Discussions, by Stefan Sylvius Wagner et al.
SQBC: Active Learning using LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions
by Stefan Sylvius Wagner, Maike Behrendt, Marc Ziegele, Stefan Harmeling
First submitted to arxiv on: 11 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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 explores novel approaches to improve stance detection in online political discussions by leveraging large language model (LLM)-generated synthetic data. It proposes two methods: first, augmenting a small fine-tuning dataset with synthetic data to boost performance; second, introducing a new active learning method called SQBC based on the “Query-by-Committee” approach. This technique uses LLM-generated synthetic data as an oracle to identify the most informative unlabelled samples for manual labelling. The results demonstrate that both methods can enhance stance detection accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows how to use artificial intelligence to better understand online political discussions. It finds ways to improve a type of AI called “stance detection” by using fake data created by large language models. Two new techniques are proposed: adding fake data to a small training set, and an “active learning” method that chooses which unlabelled samples to label manually. The results show that these methods can make stance detection more accurate. |
Keywords
» Artificial intelligence » Active learning » Fine tuning » Large language model » Synthetic data
