Summary of Using Chatgpt to Score Essays and Short-form Constructed Responses, by Mark D. Shermis
Using ChatGPT to Score Essays and Short-Form Constructed Responses
by Mark D. Shermis
First submitted to arxiv on: 18 Aug 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 performance of ChatGPT’s large language models in matching the scoring accuracy of human and machine scores from the ASAP competition. Various prediction models are examined, including linear regression, random forest, gradient boost, and boost. The investigation evaluates ChatGPT’s performance against human raters using quadratic weighted kappa (QWK) metrics. While the gradient boost model achieves QWKs close to human raters for some datasets, overall performance is inconsistent and often lower than human scores. The study highlights the need for further refinement, particularly in handling biases and ensuring scoring fairness. Despite challenges, ChatGPT demonstrates potential for scoring efficiency with domain-specific fine-tuning. The study concludes that ChatGPT can complement human scoring but requires additional development to be reliable for high-stakes assessments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ChatGPT is a big language model that helps us score things correctly. Some smart people wanted to see how good it was compared to humans and other computer models. They tested different ways of predicting scores, like using math formulas or combining lots of small predictions. When they looked at the results, they saw that ChatGPT’s best model did pretty well for some types of data, but wasn’t always as accurate as human judges. The study says we need to make it better by fixing some problems and making sure it treats everyone fairly. Even though there are challenges, ChatGPT could be helpful in scoring things quickly and accurately if we improve it. |
Keywords
» Artificial intelligence » Fine tuning » Language model » Linear regression » Random forest
