1 citations · 4 across the 26 of their papers we have counts for
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Interpreting Style Representations via Style-Eliciting Prompts
Junghwan Kim, David Jurgens
Style representation learning is a powerful tool for authorship analysis and modeling writing style, yet the latent nature of learned representations makes them difficult to interp…
Cooperative Profiles Predict Multi-Agent LLM Team Performance in AI for Science Workflows
Shivani Kumar, Adarsh Bharathwaj, David Jurgens
Multi-agent systems built from teams of large language models (LLMs) are increasingly deployed for collaborative scientific reasoning and problem-solving. These systems require age…
Think Multilingual, Not Harder: A Data-Efficient Framework for Teaching Reasoning Models to Code-Switch
Eleanor M. Lin, David Jurgens
Recent developments in reasoning capabilities have enabled large language models to solve increasingly complex mathematical, symbolic, and logical tasks. Interestingly, while reaso…
Beyond Consensus: Perspectivist Modeling and Evaluation of Annotator Disagreement in NLP
Yinuo Xu, David Jurgens
Annotator disagreement is widespread in NLP, particularly for subjective and ambiguous tasks such as toxicity detection and stance analysis. While early approaches treated disagree…
Cross-Lingual Prompt Steerability: Towards Accurate and Robust LLM Behavior across Languages
Lechen Zhang, Yusheng Zhou, Tolga Ergen +3
System prompts provide a lightweight yet powerful mechanism for conditioning large language models (LLMs) at inference time. While prior work has focused on English-only settings,…
Beyond the Explicit: A Bilingual Dataset for Dehumanization Detection in Social Media
Dennis Assenmacher, Paloma Piot, Katarina Laken +2
Digital dehumanization, although a critical issue, remains largely overlooked within the field of computational linguistics and Natural Language Processing. The prevailing approach…