565 citations · 734 across the 71 of their papers we have counts for
8 papers · 2 filters
Understanding Transformer Memorization Recall Through Idioms
Adi Haviv, Ido Cohen, Jacob Gidron +3
To produce accurate predictions, language models (LMs) must balance between generalization and memorization. Yet, little is known about the mechanism by which transformer LMs emplo…
Analyzing Transformers in Embedding Space
Guy Dar, Mor Geva, Ankit Gupta +1
Understanding Transformer-based models has attracted significant attention, as they lie at the heart of recent technological advances across machine learning. While most interpreta…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
Don't Blame the Annotator: Bias Already Starts in the Annotation Instructions
Mihir Parmar, Swaroop Mishra, Mor Geva +1
In recent years, progress in NLU has been driven by benchmarks. These benchmarks are typically collected by crowdsourcing, where annotators write examples based on annotation instr…
Inferring Implicit Relations in Complex Questions with Language Models
Uri Katz, Mor Geva, Jonathan Berant
A prominent challenge for modern language understanding systems is the ability to answer implicit reasoning questions, where the required reasoning steps for answering the question…
LM-Debugger: An Interactive Tool for Inspection and Intervention in Transformer-Based Language Models
Mor Geva, Avi Caciularu, Guy Dar +5
The opaque nature and unexplained behavior of transformer-based language models (LMs) have spurred a wide interest in interpreting their predictions. However, current interpretatio…