33 citations · 127 across the 31 of their papers we have counts for
6 papers · 1 filter
Diversity Over Quantity: A Lesson From Few Shot Relation Classification
Amir DN Cohen, Shauli Ravfogel, Shaltiel Shmidman +1
In few-shot relation classification (FSRC), models must generalize to novel relations with only a few labeled examples. While much of the recent progress in NLP has focused on scal…
Gumbel Counterfactual Generation From Language Models
Shauli Ravfogel, Anej Svete, Vésteinn Snæbjarnarson +1
Understanding and manipulating the causal generation mechanisms in language models is essential for controlling their behavior. Previous work has primarily relied on techniques suc…
GRADE: Quantifying Sample Diversity in Text-to-Image Models
Royi Rassin, Aviv Slobodkin, Shauli Ravfogel +2
We introduce GRADE, an automatic method for quantifying sample diversity in text-to-image models. Our method leverages the world knowledge embedded in large language models and vis…
On Affine Homotopy between Language Encoders
Robin SM Chan, Reda Boumasmoud, Anej Svete +8
Pre-trained language encoders -- functions that represent text as vectors -- are an integral component of many NLP tasks. We tackle a natural question in language encoder analysis:…
A Practical Method for Generating String Counterfactuals
Matan Avitan, Ryan Cotterell, Yoav Goldberg +1
Interventions targeting the representation space of language models (LMs) have emerged as an effective means to influence model behavior. Such methods are employed, for example, to…
Representation Surgery: Theory and Practice of Affine Steering
Shashwat Singh, Shauli Ravfogel, Jonathan Herzig +3
Language models often exhibit undesirable behavior, e.g., generating toxic or gender-biased text. In the case of neural language models, an encoding of the undesirable behavior is…