16 citations · 29 across the 33 of their papers we have counts for
6 papers · 1 filter
HARISSA: Inference-Time Self-Checks for Efficient and Safe Local Language Model Deployment
Kenan Alkiek, Moontae Lee, David Jurgens +1
Running a language model locally offers advantages in privacy, latency, and cost, but local hardware fits only small models, which are less capable than frontier models. The usual…
Reducing the rate of personal insults in social media with bystander bots
Libby Hemphill, Lingyao Li, Ryan Burton +1
Prompted by previous research on strategies for reducing interpersonal conflict and addressing problematic behaviors in online communities, a randomized controlled trial on Reddit…
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 Framework for Analyzing and Teaching Code-Switched Reasoning
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…