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cs.CL2026
Operadic consistency: a label-free signal for compositional reasoning failures in LLMs
Nathaniel Bottman, Yinhong Liu, Kyle Richardson
Detecting LLM reasoning failures at inference time without ground-truth labels has motivated a wide range of confidence baselines, including self-consistency, semantic entropy, and…
cs.CL2025
FaStfact: Faster, Stronger Long-Form Factuality Evaluations in LLMs
Yingjia Wan, Haochen Tan, Xiao Zhu +9
Evaluating the factuality of long-form generations from Large Language Models (LLMs) remains challenging due to efficiency bottlenecks and reliability concerns. Prior efforts attem…
cs.CL2025
Improving Preference Extraction In LLMs By Identifying Latent Knowledge Through Classifying Probes
Sharan Maiya, Yinhong Liu, Ramit Debnath +1
Large Language Models (LLMs) are often used as automated judges to evaluate text, but their effectiveness can be hindered by various unintentional biases. We propose using linear c…