4 citations · 12 across the 5 of their papers we have counts for
5 papers · 1 filter
Backward Lens: Projecting Language Model Gradients into the Vocabulary Space
Shahar Katz, Yonatan Belinkov, Mor Geva +1
Understanding how Transformer-based Language Models (LMs) learn and recall information is a key goal of the deep learning community. Recent interpretability methods project weights…
In-Context Learning Creates Task Vectors
Roee Hendel, Mor Geva, Amir Globerson
In-context learning (ICL) in Large Language Models (LLMs) has emerged as a powerful new learning paradigm. However, its underlying mechanism is still not well understood. In partic…
CRoW: Benchmarking Commonsense Reasoning in Real-World Tasks
Mete Ismayilzada, Debjit Paul, Syrielle Montariol +2
Recent efforts in natural language processing (NLP) commonsense reasoning research have yielded a considerable number of new datasets and benchmarks. However, most of these dataset…
LM vs LM: Detecting Factual Errors via Cross Examination
Roi Cohen, May Hamri, Mor Geva +1
A prominent weakness of modern language models (LMs) is their tendency to generate factually incorrect text, which hinders their usability. A natural question is whether such factu…
Crawling the Internal Knowledge-Base of Language Models
Roi Cohen, Mor Geva, Jonathan Berant +1
Language models are trained on large volumes of text, and as a result their parameters might contain a significant body of factual knowledge. Any downstream task performed by these…