Summary of Grade Like a Human: Rethinking Automated Assessment with Large Language Models, by Wenjing Xie et al.
Grade Like a Human: Rethinking Automated Assessment with Large Language Models
by Wenjing Xie, Juxin Niu, Chun Jason Xue, Nan Guan
First submitted to arxiv on: 30 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 proposed study aims to investigate the utilization of large language models (LLMs) for enhancing automated grading processes, particularly in addressing the limitations of existing research focused on predefined rubrics. The researchers emphasize that grading is a complex procedure involving multiple steps, including rubric design and post-grading review, which have not been thoroughly explored in previous studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Automated grading using LLMs has shown promise but still lags behind human performance, especially when dealing with complex questions. This study aims to explore the potential of LLMs to improve the entire grading process by examining multiple steps beyond just using predefined rubrics. The outcome could have significant implications for education. |
