234 citations · 504 across the 22 of their papers we have counts for
3 papers · 1 filter
Latent space bias directions in LLMs capture confidence, not fairness
Stephanie Buttigieg, Maeve Madigan, Parameswaran Kamalaruban +1
Activation steering has gained popularity as a lightweight inference-time debiasing technique for large language models. However, prior work reports that steering vectors generalis…
Log-Likelihood, Simpson's Paradox, and the Detection of Machine-Generated Text
Tom Kempton, Viktor Drobnyi, Maeve Madigan +1
The ability to reliably distinguish human-written text from that generated by large language models is of profound societal importance. The dominant approach to this problem exploi…
DMAP: A Distribution Map for Text
Tom Kempton, Julia Rozanova, Parameswaran Kamalaruban +5
Large Language Models (LLMs) are a powerful tool for statistical text analysis, with derived sequences of next-token probability distributions offering a wealth of information. Ext…