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)
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Summary difficulty | Written by | Summary |
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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