5 papers
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…
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,…
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-…
Large language models surpass human experts in predicting neuroscience results
Xiaoliang Luo, Akilles Rechardt, Guangzhi Sun +36
Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offe…
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…