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cs.CL2026
Are We Evaluating Knowledge or Phrasing? Mitigating MCQA Sensitivity with ParaEval
João Maria Janeiro, Mathurin Videau, Andrea Caciolai +3
Multiple-choice (MCQA) benchmarks are the standard for evaluating pretrained large language models, but their reliance on log-likelihood scoring makes them unreliable. Specifically…
cs.CL2025
SCOPE: A Self-supervised Framework for Improving Faithfulness in Conditional Text Generation
Song Duong, Florian Le Bronnec, Alexandre Allauzen +4
Large Language Models (LLMs), when used for conditional text generation, often produce hallucinations, i.e., information that is unfaithful or not grounded in the input context. Th…