3 citations · 5 across the 8 of their papers we have counts for
5 papers · 1 filter
Paying for Too Many Tokens? Valid and Cost-Efficient Multimodal LLM Annotation with Simple Heuristics
Zhixi Zhu, Kristina Gligoric
Vision-Language Models (VLMs) enable video annotation at scale, but costs accumulate quickly: processing a typical 60-second short-form video at one frame per second requires milli…
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 +6
Food safety directly affects human health in daily life, as unsafe preparation or storage can lead to contamination and foodborne illness. PwC's 2025 consumer survey reports that 4…
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…