1 citations · 1 across the 5 of their papers we have counts for
6 papers
Evaluating Persistent Calibration under Evolving Model Knowledge
Victor Wang, Thomas Hofweber, Mohit Bansal +1
As AI systems move from static repositories to agents that are capable of continual adaptation and learning, maintaining their trustworthiness means equipping the models backing th…
SHARD: Safe and Helpful Alignment via Self-Reframing Distillation
Viswonathan Manoranjan, Amogh Gupta, Anvesh Rao Vijjini +2
Large language models often struggle with sensitive prompts. They may refuse outright, provide generic safety boilerplate, or fail to address the user's legitimate informational ne…
AI-Mediated Explainable Regulation for Justice
Thomas Hofweber, Andreas Sudmann, Evangelos Pournaras
Present practice of deciding on regulation faces numerous problems that make adopted regulations static, unexplained, unduly influenced by powerful interest groups, and stained wit…
The Black Tuesday Attack: how to crash the stock market with adversarial examples to financial forecasting models
Thomas Hofweber, Jefrey Bergl, Ian Reyes +1
We investigate and defend the possibility of causing a stock market crash via small manipulations of individual stock values that together realize an adversarial example to financi…
Fundamental Problems With Model Editing: How Should Rational Belief Revision Work in LLMs?
Peter Hase, Thomas Hofweber, Xiang Zhou +2
The model editing problem concerns how language models should learn new facts about the world over time. While empirical research on model editing has drawn widespread attention, t…
Are language models rational? The case of coherence norms and belief revision
Thomas Hofweber, Peter Hase, Elias Stengel-Eskin +1
Do norms of rationality apply to machine learning models, in particular language models? In this paper we investigate this question by focusing on a special subset of rational norm…