3 citations · 5 across the 8 of their papers we have counts for
10 papers
Mitigating LLM-based p-Hacking by Preregistering for the Next LLM
Maria Thomas, Kristina Gligoric, Nihar B. Shah
Large language models (LLMs) are increasingly used to generate, classify, and annotate data whose outputs feed downstream hypothesis tests. However, LLM-based research is easy to p…
Cooking Up Risks: Benchmarking and Reducing Food Safety Risks in Large Language Models
Weidi Luo, Xiaofei Wen, Tenghao Huang +5
Large language models (LLMs) are increasingly deployed for everyday tasks, including food preparation and health-related guidance. However, food safety remains a high-stakes domain…
SWAY: A Counterfactual Computational Linguistic Approach to Measuring and Mitigating Sycophancy
Joy Bhalla, Kristina Gligorić
Large language models exhibit sycophancy: the tendency to shift outputs toward user-expressed stances, regardless of correctness or consistency. While prior work has studied this i…
Multi-Perspective LLM Annotations for Valid Analyses in Subjective Tasks
Navya Mehrotra, Adam Visokay, Kristina Gligorić
Large language models are increasingly used to annotate texts, but their outputs reflect some human perspectives better than others. Existing methods for correcting LLM annotation…
Valid Survey Simulations with Limited Human Data: The Roles of Prompting, Fine-Tuning, and Rectification
Stefan Krsteski, Giuseppe Russo, Serina Chang +2
Surveys provide valuable insights into public opinion and behavior, but their execution is costly and slow. Large language models (LLMs) have been proposed as a scalable, low-cost…
Attention to Non-Adopters
Kaitlyn Zhou, Kristina Gligorić, Myra Cheng +7
Although language model-based chat systems are increasingly used in daily life, most Americans remain non-adopters of chat-based LLMs -- as of June 2025, 66% had never used ChatGPT…