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cs.CL2025
Correlated Errors in Large Language Models
Elliot Kim, Avi Garg, Kenny Peng +1
Diversity in training data, architecture, and providers is assumed to mitigate homogeneity in LLMs. However, we lack empirical evidence on whether different LLMs differ meaningfull…
cs.LG2025★ 2 cited
Position: Use Sparse Autoencoders to Discover Unknowns
Kenny Peng, Rajiv Movva, Jon Kleinberg +2
While sparse autoencoders (SAEs) have generated significant excitement, a series of negative results have added to skepticism about their usefulness. Here, we establish a conceptua…
cs.CL2025
Sparse Autoencoders for Hypothesis Generation
Rajiv Movva, Kenny Peng, Nikhil Garg +2
We describe HypotheSAEs, a general method to hypothesize interpretable relationships between text data (e.g., headlines) and a target variable (e.g., clicks). HypotheSAEs has three…