Using Language Models to Detect Alarming Student Responses
May 12, 2023 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
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Authors
Christopher M. Ormerod, Milan Patel, Harry Wang
arXiv ID
2305.07709
Category
cs.CL: Computation & Language
Cross-listed
cs.IR,
cs.LG
Citations
0
Venue
arXiv.org
Last Checked
6 months ago
Abstract
This article details the advances made to a system that uses artificial intelligence to identify alarming student responses. This system is built into our assessment platform to assess whether a student's response indicates they are a threat to themselves or others. Such responses may include details concerning threats of violence, severe depression, suicide risks, and descriptions of abuse. Driven by advances in natural language processing, the latest model is a fine-tuned language model trained on a large corpus consisting of student responses and supplementary texts. We demonstrate that the use of a language model delivers a substantial improvement in accuracy over the previous iterations of this system.
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