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A comparative study of BART, BERT, and RoBERTa for text summarization, examining their architectures and suitability for extractive and abstractive summarization tasks.
This paper compares RoBERTa-based sentiment analysis with an LLM-based multi-dimensional framing analysis on political news articles, finding that traditional SA suffers from 'neutral collapse' and that LLM-based approaches better capture bias, sensationalism, and framing for social science research.
Proposes a Hierarchy-Aware RoBERTa framework for classifying cybersecurity vulnerabilities in the CWE taxonomy, demonstrating that hierarchy-aware representation learning is more effective than oversampling techniques for handling class imbalance.
This paper presents UCSC NLP's systems for SemEval-2026 Task 10 (PsyCoMark), addressing conspiracy marker extraction using boundary-aware span extraction with RoBERTa, and document-level conspiracy classification with label smoothing. The systems ranked 7th in subtask 1 and 12th in subtask 2.
This paper proposes a dual-threshold hard example mining strategy for cross-platform Chinese offensive comment detection, addressing performance degradation due to domain shift. The method fine-tunes a RoBERTa model on the COLD dataset and adapts it to four Chinese social media platforms with minimal labeled data.
HierBias introduces a hierarchical context-conditioned model for media bias detection that leverages document context to improve sentence-level classification, achieving state-of-the-art F1 and MCC on the BABE and BASIL datasets.
A fine-grained study of narrative features in web-scale LLM pretraining data, introducing NarraBERT and NarraDolma to measure narrative patterns and their distribution across sources.
This paper introduces a controlled content overlap setup using parallel Bible translations to evaluate how much style classifiers rely on content cues rather than actual style features. Results show that low-overlap models degrade when content cues are removed, while high-overlap models transfer more robustly.
This paper demonstrates that fine-tuned AI text detectors amplify a pretrained typicality axis rather than learning an AI-vs-human boundary, with raw encoder projections often matching or exceeding fine-tuned performance.
ETH Zurich researchers show that fine-tuned RoBERTa models can infer users’ Big-Five personality traits from ChatGPT chat logs with up to 44 % above-random accuracy, highlighting privacy risks of conversational AI.