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Zoom In Disparities in Healthcare LLM Q&A
Ipek Baris Schlicht, Burcu Sayin, Zhixue Zhao +5
Equitable access to reliable health information is vital when integrating AI into healthcare. Yet, information quality varies across languages, raising concerns about the reliabili…
Reinforcement Learning Amplifies Emergent Misalignment from Harmless Rewards
Magnus Jørgenvåg, David Kaczér, Lasse Ruttert +3
Emergent misalignment (EM) is the surprising tendency of language models to become broadly misaligned after fine-tuning on narrowly misaligned examples. While EM has been extensive…
Can LLM Agents Identify Spoken Dialects like a Linguist?
Tobias Bystrich, Lukas Hamm, Maria Hassan +3
Due to the scarcity of labeled dialectal speech, audio dialect classification is a challenging task for most languages, including Swiss German. In this work, we explore the ability…
Conspiracy Frame: a Semiotically-Driven Approach for Conspiracy Theories Detection
Heidi Campana Piva, Shaina Ashraf, Maziar Kianimoghadam Jouneghani +4
Conspiracy theories are anti-authoritarian narratives that lead to social conflict, impacting how people perceive political information. To help in understanding this issue, we int…
More Agents Improve Math Problem Solving but Adversarial Robustness Gap Persists
Khashayar Alavi, Zhastay Yeltay, Lucie Flek +1
When LLM agents work together, they seem to be more powerful than a single LLM in mathematical question answering. However, are they also more robust to adversarial inputs? We inve…
ArithmAttack: Evaluating Robustness of LLMs to Noisy Context in Math Problem Solving
Zain Ul Abedin, Shahzeb Qamar, Lucie Flek +1
While Large Language Models (LLMs) have shown impressive capabilities in math problem-solving tasks, their robustness to noisy inputs is not well-studied. We propose ArithmAttack t…