From the 1 of 4 linked papers with an AI index.
4 papers
What Does It Mean to Break a Distillation Defense?
Lena Libon, Pura Peetathawatchai, Michael Aerni +2
The paper examines how to evaluate defenses that add noise to large language model outputs to thwart distillation attacks, proposing a three‑dimensional threat model (query budget,…
Large-scale online deanonymization with LLMs
Simon Lermen, Daniel Paleka, Joshua Swanson +3
We show that large language models can be used to perform at-scale deanonymization. With full Internet access, our agent can re-identify Hacker News users and Anthropic Interviewer…
Pitfalls in Evaluating Language Model Forecasters
Daniel Paleka, Shashwat Goel, Jonas Geiping +1
Large language models (LLMs) have recently been applied to forecasting tasks, with some works claiming these systems match or exceed human performance. In this paper, we argue that…
Consistency Checks for Language Model Forecasters
Daniel Paleka, Abhimanyu Pallavi Sudhir, Alejandro Alvarez +4
Forecasting is a task that is difficult to evaluate: the ground truth can only be known in the future. Recent work showing LLM forecasters rapidly approaching human-level performan…