565 citations · 2.5k across the 94 of their papers we have counts for
11 papers · 1 filter
When Language Models Fall in Love: Animacy Processing in Transformer Language Models
Michael Hanna, Yonatan Belinkov, Sandro Pezzelle
Animacy - whether an entity is alive and sentient - is fundamental to cognitive processing, impacting areas such as memory, vision, and language. However, animacy is not always exp…
Unified Concept Editing in Diffusion Models
Rohit Gandikota, Hadas Orgad, Yonatan Belinkov +2
Text-to-image models suffer from various safety issues that may limit their suitability for deployment. Previous methods have separately addressed individual issues of bias, copyri…
Linearity of Relation Decoding in Transformer Language Models
Evan Hernandez, Arnab Sen Sharma, Tal Haklay +5
Much of the knowledge encoded in transformer language models (LMs) may be expressed in terms of relations: relations between words and their synonyms, entities and their attributes…
Instructed to Bias: Instruction-Tuned Language Models Exhibit Emergent Cognitive Bias
Itay Itzhak, Gabriel Stanovsky, Nir Rosenfeld +1
Recent studies show that instruction tuning (IT) and reinforcement learning from human feedback (RLHF) improve the abilities of large language models (LMs) dramatically. While thes…
Generating Benchmarks for Factuality Evaluation of Language Models
Dor Muhlgay, Ori Ram, Inbal Magar +7
Before deploying a language model (LM) within a given domain, it is important to measure its tendency to generate factually incorrect information in that domain. Existing methods f…
ReFACT: Updating Text-to-Image Models by Editing the Text Encoder
Dana Arad, Hadas Orgad, Yonatan Belinkov
Our world is marked by unprecedented technological, global, and socio-political transformations, posing a significant challenge to text-to-image generative models. These models enc…
Shielded Representations: Protecting Sensitive Attributes Through Iterative Gradient-Based Projection
Shadi Iskander, Kira Radinsky, Yonatan Belinkov
Natural language processing models tend to learn and encode social biases present in the data. One popular approach for addressing such biases is to eliminate encoded information f…
A Mechanistic Interpretation of Arithmetic Reasoning in Language Models using Causal Mediation Analysis
Alessandro Stolfo, Yonatan Belinkov, Mrinmaya Sachan
Mathematical reasoning in large language models (LMs) has garnered significant attention in recent work, but there is a limited understanding of how these models process and store…
VISIT: Visualizing and Interpreting the Semantic Information Flow of Transformers
Shahar Katz, Yonatan Belinkov
Recent advances in interpretability suggest we can project weights and hidden states of transformer-based language models (LMs) to their vocabulary, a transformation that makes the…
ContraSim -- Analyzing Neural Representations Based on Contrastive Learning
Adir Rahamim, Yonatan Belinkov
Recent work has compared neural network representations via similarity-based analyses to improve model interpretation. The quality of a similarity measure is typically evaluated by…
Editing Implicit Assumptions in Text-to-Image Diffusion Models
Hadas Orgad, Bahjat Kawar, Yonatan Belinkov
Text-to-image diffusion models often make implicit assumptions about the world when generating images. While some assumptions are useful (e.g., the sky is blue), they can also be o…