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cs.CL2025
Probability Consistency in Large Language Models: Theoretical Foundations Meet Empirical Discrepancies
Xiaoliang Luo, Xinyi Xu, Michael Ramscar +1
Can autoregressive large language models (LLMs) learn consistent probability distributions when trained on sequences in different token orders? We prove formally that for any well-…
cs.CL2024
Beyond Human-Like Processing: Large Language Models Perform Equivalently on Forward and Backward Scientific Text
Xiaoliang Luo, Michael Ramscar, Bradley C. Love
The impressive performance of large language models (LLMs) has led to their consideration as models of human language processing. Instead, we suggest that the success of LLMs arise…