Summary of Says Who? Effective Zero-shot Annotation Of Focalization, by Rebecca M. M. Hicke et al.
Says Who? Effective Zero-Shot Annotation of Focalization
by Rebecca M. M. Hicke, Yuri Bizzoni, Pascale Feldkamp, Ross Deans Kristensen-McLachlan
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 This paper investigates how well Large Language Models (LLMs) perform when annotating literary texts for focalization mode. Focalization refers to the perspective through which a narrative is presented, and it’s encoded via various lexico-grammatical features. The study finds that LLMs can accurately identify focalization modes in literary texts, comparable to trained human annotators. The authors provide experiments using Stephen King novels as case studies, demonstrating the potential of this approach for computational literary studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks at how well computers can understand the way stories are told. In storytelling, the perspective or viewpoint is important. This paper tests if computers (called Large Language Models) can identify these perspectives in books as accurately as people who have been trained to do so. The results show that the computers can do a good job of identifying these perspectives, similar to how humans do it. The study uses famous author Stephen King’s books as examples to demonstrate how this approach can be used to analyze literature. |
