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cs.CL2024
PertEval: Unveiling Real Knowledge Capacity of LLMs with Knowledge-Invariant Perturbations
Jiatong Li, Renjun Hu, Kunzhe Huang +5
Expert-designed close-ended benchmarks are indispensable in assessing the knowledge capacity of large language models (LLMs). Despite their widespread use, concerns have mounted re…
cs.CL2023★ 15 cited
Position: AI Evaluation Should Learn from How We Test Humans
Yan Zhuang, Qi Liu, Zachary A. Pardos +5
As AI systems continue to evolve, their rigorous evaluation becomes crucial for their development and deployment. Researchers have constructed various large-scale benchmarks to det…