7 citations · 7 across the 6 of their papers we have counts for
9 papers
The Unequal Influence of Bad Advice: Using Training Data Attribution to Modulate Emergent Misalignment
Gonçalo Paulo, Louis Jaburi, Nora Belrose +2
Fine-tuning large language models on narrow, misaligned tasks can undo their post-training alignment and induce novel misaligned behaviors -- a phenomenon known as \emph{emergent m…
Can Data Attribution Filter Out Subliminal Learning? Not Reliably
Moritz Weckbecker, Sweta Jena, Jonas Müller +5
Subliminal learning allows language models to transmit behavioral traits through training data with no obvious semantic relationship to those traits, undermining content-based data…
Capability Provenance in Language Models: A Case Study in Social Reasoning
Glenn Matlin, Chandreyi Chakraborty, Saehee Eom +8
We use training-data attribution as an interpretable tool for capability discovery, mapping which regions of the pretraining corpus support social reasoning versus STEM reasoning i…
Bergson: An Open Source Library for Data Attribution
Lucia Quirke, Louis Jaburi, David Johnston +6
Data attribution is a promising field in interpretability that aims to explain model behavior through the influence of its training data, with applications including debugging unde…
Concept Influence: Leveraging Interpretability to Improve Performance and Efficiency in Training Data Attribution
Matthew Kowal, Goncalo Paulo, Louis Jaburi +6
As large language models are increasingly trained and fine-tuned, practitioners need methods to identify which training data drive specific behaviors, particularly unintended ones.…
Evaluating Explanations: An Explanatory Virtues Framework for Mechanistic Interpretability -- The Strange Science Part I.ii
Kola Ayonrinde, Louis Jaburi
Mechanistic Interpretability (MI) aims to understand neural networks through causal explanations. Though MI has many explanation-generating methods, progress has been limited by th…
A Mathematical Philosophy of Explanations in Mechanistic Interpretability -- The Strange Science Part I.i
Kola Ayonrinde, Louis Jaburi
Mechanistic Interpretability aims to understand neural networks through causal explanations. We argue for the Explanatory View Hypothesis: that Mechanistic Interpretability researc…
Towards a unified and verified understanding of group-operation networks
Wilson Wu, Louis Jaburi, Jacob Drori +1
A recent line of work in mechanistic interpretability has focused on reverse-engineering the computation performed by neural networks trained on the binary operation of finite grou…
Functions on Irreducible Components of the Emerton-Gee Stack
Eivind Otto Hjelle, Louis Jaburi, Rachel Knak +2
Let be a finite unramified extension, and let denote the Emerton-Gee stack parametrizing étale -modules of rank . It is known since t…