1.8k citations · 2.9k across the 27 of their papers we have counts for
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Speciesism in AI: Evaluating Discrimination Against Animals in Large Language Models
Monika Jotautaitė, Lucius Caviola, David A. Brewster +1
As large language models (LLMs) become more widely deployed, it is crucial to examine their ethical tendencies. Building on research on fairness and discrimination in AI, we invest…
Large Reasoning Models Are Autonomous Jailbreak Agents
Thilo Hagendorff, Erik Derner, Nuria Oliver
Jailbreaking -- bypassing built-in safety mechanisms in AI models -- has traditionally required complex technical procedures or specialized human expertise. In this study, we show…
On the Inevitability of Left-Leaning Political Bias in Aligned Language Models
Thilo Hagendorff
The guiding principle of AI alignment is to train large language models (LLMs) to be harmless, helpful, and honest (HHH). At the same time, there are mounting concerns that LLMs ex…
PRIDE -- Parameter-Efficient Reduction of Identity Discrimination for Equality in LLMs
Maluna Menke, Thilo Hagendorff
Large Language Models (LLMs) frequently reproduce the gender- and sexual-identity prejudices embedded in their training corpora, leading to outputs that marginalize LGBTQIA+ users.…
Beyond Chains of Thought: Benchmarking Latent-Space Reasoning Abilities in Large Language Models
Thilo Hagendorff, Sarah Fabi
Large language models (LLMs) can perform reasoning computations both internally within their latent space and externally by generating explicit token sequences like chains of thoug…
Compromising Honesty and Harmlessness in Language Models via Deception Attacks
Laurène Vaugrante, Francesca Carlon, Maluna Menke +1
Recent research on large language models (LLMs) has demonstrated their ability to understand and employ deceptive behavior, even without explicit prompting. However, such behavior…