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cs.CL2026
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…
cs.CL2026
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…
cs.CL2026
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…