Itay Yona — accepted AI-conference papers

2026

Extracting alignment data in open models

Federico Barbero, Xiangming Gu, Christopher A. Choquette Choo, Chawin Sitawarin, Matthew Jagielski, Itay Yona +3

ICML 2026poster

In this work, we show that it is possible to extract significant amounts of alignment training data from a post-trained model -- useful to steer the model to improve certain capabilities such as long-context reasoning, safety, instruction following, and maths. While the majority of related work on m…

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2025

Interpreting the Repeated Token Phenomenon in Large Language Models

Itay Yona, Ilia Shumailov, Jamie Hayes, Yossi Gandelsman

ICML 2025poster

Large Language Models (LLMs), despite their impressive capabilities, often fail to accurately repeat a single word when prompted to, and instead output unrelated text. This unexplained failure mode represents a *vulnerability*, allowing even end users to diverge models away from their intended behav…

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2025

Measuring memorization in language models via probabilistic extraction

Jamie Hayes, Marika Swanberg, Harsh Chaudhari, Itay Yona, Ilia Shumailov, Milad Nasr +3

NAACL 2025long

Large language models (LLMs) are susceptible to memorizing training data, raising concerns about the potential extraction of sensitive information at generation time. Discoverable extraction is the most common method for measuring this issue: split a training example into a prefix and suffix, then p…

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