8 citations · 27 across the 15 of their papers we have counts for
8 papers · 1 filter
A Robust Evaluation of Probe Robustness: Lessons for Reliable OOD Uncertainty Quantification
Joe Stacey, Hadas Orgad, Kentaro Inui +2
Recent work has shown that the hidden states of large language models contain signals useful for uncertainty estimation, motivating a growing interest in efficient probe-based appr…
Large Language Models Generate Harmful Responses Using a Distinct Mechanism, Shared Across Harm Types
Hadas Orgad, Boyi Wei, Kaden Zheng +4
Large language models remain vulnerable to jailbreaks that elicit harmful responses, yet the mechanism behind harmful response generation is poorly understood. Here, we investigate…
Padding Tone: A Mechanistic Analysis of Padding Tokens in T2I Models
Michael Toker, Ido Galil, Hadas Orgad +4
Text-to-image (T2I) diffusion models rely on encoded prompts to guide the image generation process. Typically, these prompts are extended to a fixed length by adding padding tokens…
LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinations
Hadas Orgad, Michael Toker, Zorik Gekhman +4
Large language models (LLMs) often produce errors, including factual inaccuracies, biases, and reasoning failures, collectively referred to as "hallucinations". Recent studies have…
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…
BLIND: Bias Removal With No Demographics
Hadas Orgad, Yonatan Belinkov
Models trained on real-world data tend to imitate and amplify social biases. Common methods to mitigate biases require prior information on the types of biases that should be mitig…
Choose Your Lenses: Flaws in Gender Bias Evaluation
Hadas Orgad, Yonatan Belinkov
Considerable efforts to measure and mitigate gender bias in recent years have led to the introduction of an abundance of tasks, datasets, and metrics used in this vein. In this pos…
How Gender Debiasing Affects Internal Model Representations, and Why It Matters
Hadas Orgad, Seraphina Goldfarb-Tarrant, Yonatan Belinkov
Common studies of gender bias in NLP focus either on extrinsic bias measured by model performance on a downstream task or on intrinsic bias found in models' internal representation…