Summary of Automated Text Scoring in the Age Of Generative Ai For the Gpu-poor, by Christopher Michael Ormerod et al.
Automated Text Scoring in the Age of Generative AI for the GPU-poor
by Christopher Michael Ormerod, Alexander Kwako
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 explores the potential of open-source, small-scale generative language models (GLMs) for automated text scoring (ATS). Unlike previous research that relied on proprietary models accessed through Application Programming Interfaces (APIs), this study focuses on using modest, consumer-grade hardware to fine-tune GLMs. The results show that GLMs can achieve adequate performance, though not state-of-the-art, and the authors also investigate the capacity of these models for generating feedback by prompting them to explain their scores. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, researchers are looking at how small, open-source language models can be used to score written text. They’re finding that these models can do a good job, but not as well as bigger, more powerful ones. The study also looks at whether these models can provide helpful feedback on why they scored certain texts the way they did. |
Keywords
* Artificial intelligence * Prompting
