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Large-scale study finds LLM agents can predict individual social-media reactions with 70.7 % accuracy but still lag behind simple TF-IDF classifiers, highlighting both manipulation risks and policy-simulation utility.
This paper presents a hybrid framework for detecting alarming or distressed student verbal responses by combining a text classifier (content-based) and an audio classifier (prosodic features), aimed at expediting human review in Automated Verbal Response Scoring systems. The approach addresses a safety gap in automated scoring pipelines where at-risk student responses may otherwise go unnoticed.
OpenAI has released a preliminary AI text classifier designed to help identify AI-written content, with a focus on supporting educators, journalists, and misinformation researchers. The tool comes with acknowledged limitations and is accompanied by an educational resource for teachers on ChatGPT's uses and constraints.
This paper presents adversarial and virtual adversarial training methods adapted for text classification by applying perturbations to word embeddings in RNNs rather than raw inputs. The approach achieves state-of-the-art results on semi-supervised and supervised text classification benchmarks while reducing overfitting.