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cs.CL2026
When Is Noise Response Universal? Tokenization as the Hidden Variable in Language Models
Yefan Tao, Gerald Friedland, Luyang Kong
The performance of textual neural models often degrades when their inputs are corrupted by noise such as typos, OCR errors, or dropped words. We study the degradation rate across n…
cs.CL2024
PPLqa: An Unsupervised Information-Theoretic Quality Metric for Comparing Generative Large Language Models
Gerald Friedland, Xin Huang, Yueying Cui +3
We propose PPLqa, an easy to compute, language independent, information-theoretic metric to measure the quality of responses of generative Large Language Models (LLMs) in an unsupe…