Summary of Can Gpt-4 Learn to Analyse Moves in Research Article Abstracts?, by Danni Yu et al.
Can GPT-4 learn to analyse moves in research article abstracts?
by Danni Yu, Marina Bondi, Ken Hyland
First submitted to arxiv on: 22 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 This paper proposes an innovative approach to analyzing written discourse by employing GPT-4 to automate the annotation process. The researchers develop natural language prompts that enable the model to identify distinct communicative acts, or “moves,” in abstracts from applied linguistics journals. The annotated outputs are evaluated by human assessors, demonstrating the potential of GPT-4 in reducing bias and enhancing accuracy. The study confirms that including examples illustrating areas of variability can improve the model’s ability to recognize multiple moves in a single sentence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses a special computer program called GPT-4 to help analyze how writers use different styles to communicate their ideas. Researchers created specific instructions, or “prompts,” for the program to identify patterns in how writers structure their texts. They tested these prompts on abstracts from four journals and found that the program got better at recognizing these patterns when it was given examples of how the patterns can vary. This could be a useful tool for people who study writing and language, as it helps reduce human error and improves accuracy. |
Keywords
» Artificial intelligence » Discourse » Gpt
