72 citations · 98 across the 14 of their papers we have counts for
6 papers · 1 filter
The Geometry of Concepts: Sparse Autoencoder Feature Structure
Yuxiao Li, Eric J. Michaud, David D. Baek +3
Sparse autoencoders have recently produced dictionaries of high-dimensional vectors corresponding to the universe of concepts represented by large language models. We find that thi…
Decomposing The Dark Matter of Sparse Autoencoders
Joshua Engels, Logan Riggs, Max Tegmark
Sparse autoencoders (SAEs) are a promising technique for decomposing language model activations into interpretable linear features. However, current SAEs fall short of completely e…
Investigating Representation Universality: Case Study on Genealogical Representations
David D. Baek, Yuxiao Li, Max Tegmark
Motivated by interpretability and reliability, we investigate whether large language models (LLMs) deploy universal geometric structures to encode discrete, graph-structured knowle…
Efficient Dictionary Learning with Switch Sparse Autoencoders
Anish Mudide, Joshua Engels, Eric J. Michaud +2
Sparse autoencoders (SAEs) are a recent technique for decomposing neural network activations into human-interpretable features. However, in order for SAEs to identify all features…
DafnyBench: A Benchmark for Formal Software Verification
Chloe Loughridge, Qinyi Sun, Seth Ahrenbach +7
We introduce DafnyBench, the largest benchmark of its kind for training and evaluating machine learning systems for formal software verification. We test the ability of LLMs such a…
Black-Box Access is Insufficient for Rigorous AI Audits
Stephen Casper, Carson Ezell, Charlotte Siegmann +18
External audits of AI systems are increasingly recognized as a key mechanism for AI governance. The effectiveness of an audit, however, depends on the degree of access granted to a…