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XYBench: Can LLMs Respond Pragmatically to Queries with Misconceptions?
Akhila Yerukola, Jena D. Hwang, Mingqian Zheng +5
When non-expert users ask LLMs for assistance, their queries can often have misconceptions (e.g., "How do I parse XML with regex?"). In such cases, often referred to as the XY-prob…
Useless but Safe? Benchmarking Utility Recovery with User Intent Clarification in Multi-Turn Conversations
Mingqian Zheng, Malia Morgan, Liwei Jiang +2
Current LLM safety alignment techniques improve model robustness against adversarial attacks, but overlook whether and how LLMs can recover helpfulness when benign users clarify th…
Synthetic Socratic Debates: Examining Persona Effects on Moral Decision and Persuasion Dynamics
Jiarui Liu, Yueqi Song, Yunze Xiao +5
As large language models (LLMs) are increasingly used in morally sensitive domains, it is crucial to understand how persona traits affect their moral reasoning and persuasive behav…
Let Them Down Easy! Contextual Effects of LLM Guardrails on User Perceptions and Preferences
Mingqian Zheng, Wenjia Hu, Patrick Zhao +5
Current LLMs are trained to refuse potentially harmful input queries regardless of whether users actually had harmful intents, causing a tradeoff between safety and user experience…