3 papers
cs.LG2025
Evaluating Sparse Autoencoders for Monosemantic Representation
Moghis Fereidouni, Muhammad Umair Haider, Peizhong Ju +1
A key barrier to interpreting large language models is polysemanticity, where neurons activate for multiple unrelated concepts. Sparse autoencoders (SAEs) have been proposed to mit…
cs.HC2025
Confidence-weighted integration of human and machine judgments for superior decision-making
Felipe Yáñez, Xiaoliang Luo, Omar Valerio Minero +1
Large language models (LLMs) can surpass humans in certain forecasting tasks. What role does this leave for humans in the overall decision process? One possibility is that humans,…
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-…