Reinforcing Stereotypes of Anger: Emotion AI on African American Vernacular English

This paper investigates the automated classification of exam questions and learning outcomes based on Bloom's Taxonomy. A dataset of 600 sentences was categorized into six cognitive levels: Knowledge, Comprehension, Application, Analysis, Synthesis, and Evaluation. Various machine learning models, including traditional methods and large language models, were evaluated, with Support Vector Machines achieving the highest accuracy of 94%, while RNN models and BERT faced significant overfitting issues.

The study examines the performance of pretrained transformer-encoder models, specifically ModernBERT and DeBERTaV3. While ModernBERT claims improved performance on various benchmarks, the lack of shared training data complicates the assessment of these gains. A controlled study pretraining ModernBERT on the same dataset as CamemBERTaV2 reveals that DeBERTaV3 outperforms ModernBERT in sample efficiency and overall benchmark performance, although ModernBERT offers advantages in long context support and training speed.

Personal attacks have increasingly characterized U.S. presidential debates, influencing public perception during elections. This study presents a framework for analyzing such attacks using manual annotation of debate transcripts from the 2016, 2020, and 2024 election cycles. By leveraging advancements in deep learning, particularly BERT and large language models, the research aims to enhance the detection of harmful language in political discourse, providing valuable insights for journalists and the public.

The article discusses a novel approach to in-context learning (ICL) for text classification, emphasizing the importance of selecting appropriate demonstrations. Traditional methods often prioritize semantic similarity, neglecting label distribution alignment, which can impact performance. The proposed method, TopK + Label Distribution Divergence (L2D), utilizes a fine-tuned BERT-like small language model to generate label distributions and assess their divergence. This dual focus aims to enhance the effectiveness of demonstration selection in large language models (LLMs).