11 citations · 17 across the 9 of their papers we have counts for
5 papers · 1 filter
Bayesian scaling laws for in-context learning
Aryaman Arora, Dan Jurafsky, Christopher Potts +1
In-context learning (ICL) is a powerful technique for getting language models to perform complex tasks with no training updates. Prior work has established strong correlations betw…
pyvene: A Library for Understanding and Improving PyTorch Models via Interventions
Zhengxuan Wu, Atticus Geiger, Aryaman Arora +5
Interventions on model-internal states are fundamental operations in many areas of AI, including model editing, steering, robustness, and interpretability. To facilitate such resea…
Backtracing: Retrieving the Cause of the Query
Rose E. Wang, Pawan Wirawarn, Omar Khattab +2
Many online content portals allow users to ask questions to supplement their understanding (e.g., of lectures). While information retrieval (IR) systems may provide answers for suc…
Automated Statistical Model Discovery with Language Models
Michael Y. Li, Emily B. Fox, Noah D. Goodman
Statistical model discovery is a challenging search over a vast space of models subject to domain-specific constraints. Efficiently searching over this space requires expertise in…
A Reply to Makelov et al. (2023)'s "Interpretability Illusion" Arguments
Zhengxuan Wu, Atticus Geiger, Jing Huang +4
We respond to the recent paper by Makelov et al. (2023), which reviews subspace interchange intervention methods like distributed alignment search (DAS; Geiger et al. 2023) and cla…