6 citations · 6 across the 4 of their papers we have counts for
5 papers · 1 filter
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…
Hidden Failures in Robustness: Why Supervised Uncertainty Quantification Needs Better Evaluation
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 and hallucination detection, motivating a growing interest i…
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…
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…
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…