5 papers · 1 filter
Continual Memorization of Factoids in Language Models
Howard Chen, Jiayi Geng, Adithya Bhaskar +2
As new knowledge rapidly accumulates, language models (LMs) with pretrained knowledge quickly become obsolete. A common approach to updating LMs is fine-tuning them directly on new…
Extracting Rule-based Descriptions of Attention Features in Transformers
Dan Friedman, Adithya Bhaskar, Alexander Wettig +1
Mechanistic interpretability strives to explain model behavior in terms of bottom-up primitives. The leading paradigm is to express hidden states as a sparse linear combination of…
Finding Transformer Circuits with Edge Pruning
Adithya Bhaskar, Alexander Wettig, Dan Friedman +1
The path to interpreting a language model often proceeds via analysis of circuits -- sparse computational subgraphs of the model that capture specific aspects of its behavior. Rece…
Representing Rule-based Chatbots with Transformers
Dan Friedman, Abhishek Panigrahi, Danqi Chen
What kind of internal mechanisms might Transformers use to conduct fluid, natural-sounding conversations? Prior work has illustrated by construction how Transformers can solve vari…
The Heuristic Core: Understanding Subnetwork Generalization in Pretrained Language Models
Adithya Bhaskar, Dan Friedman, Danqi Chen
Prior work has found that pretrained language models (LMs) fine-tuned with different random seeds can achieve similar in-domain performance but generalize differently on tests of s…