Summary of Predicting Emotion Intensity in Polish Political Texts: Comparing Supervised Models and Large Language Models in a Resource-poor Language, by Hubert Plisiecki et al.
Predicting Emotion Intensity in Polish Political Texts: Comparing Supervised Models and Large Language Models in a Resource-Poor Language
by Hubert Plisiecki, Piotr Koc, Maria Flakus, Artur Pokropek
First submitted to arxiv on: 16 Jul 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 The study investigates the use of large language models (LLMs) in predicting emotion intensity in Polish political texts, a low-resource language context. It compares the performance of several LLMs with a supervised model trained on an annotated corpus of social media texts, evaluated for emotion intensity by expert judges. The results show that while the supervised model generally outperforms LLMs, offering higher accuracy and lower variance, LLMs present a viable alternative, especially considering the high costs associated with data annotation. This highlights the potential of LLMs in low-resource language settings and underscores the need for further research on emotion intensity prediction and its application across different languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores how computers can understand emotions in Polish political texts. Researchers compared different computer models to predict how intense certain emotions are in these texts. They found that a special model trained on social media texts is best, but other models that don’t need training data can still do well. This shows that even when we have limited language resources, there are ways for computers to understand and analyze emotions. |
Keywords
» Artificial intelligence » Supervised
