Summary of Evaluating Generative Ai-enhanced Content: a Conceptual Framework Using Qualitative, Quantitative, and Mixed-methods Approaches, by Saman Sarraf
Evaluating Generative AI-Enhanced Content: A Conceptual Framework Using Qualitative, Quantitative, and Mixed-Methods Approaches
by Saman Sarraf
First submitted to arxiv on: 26 Nov 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 manuscript explores the application of qualitative, quantitative, and mixed-methods research approaches to evaluate the performance of generative AI (GenAI) models in enhancing scientific writing. Using a hypothetical use case involving a collaborative medical imaging manuscript, the study demonstrates how each method provides unique insights into the impact of GenAI. The authors employ thematic analysis tools to capture nuanced improvements and identify limitations using qualitative methods. Quantitative approaches involve automated metrics such as BLEU, ROUGE, and readability scores, as well as user surveys, to objectively measure improvements in coherence, fluency, and structure. Mixed-methods research integrates these strengths, combining statistical evaluations with detailed qualitative insights to provide a comprehensive assessment. The study enables quantifying improvement levels in GenAI-generated content, addressing critical aspects of linguistic quality and technical accuracy. Furthermore, it offers a robust framework for benchmarking GenAI tools against traditional editing processes, ensuring the reliability and effectiveness of these technologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how to test if AI can help make scientific writing better. They use three different ways to measure how well AI does this: looking at expert feedback, using computers to count words and sentences, and asking people what they think. They tested it on a pretend medical imaging report and found that each method gives us different information. The results show that AI can really help make scientific writing better by making it clearer and more organized. This study helps us figure out how well AI does this job and how we can use it in real-life situations like medicine and science. |
Keywords
» Artificial intelligence » Bleu » Rouge
