77 citations · 172 across the 20 of their papers we have counts for
6 papers · 1 filter
The LLM Fallacy: Misattribution in AI-Assisted Cognitive Workflows
Hyunwoo Kim, Harin Yu, Hanau Yi
The rapid integration of large language models (LLMs) into everyday workflows has transformed how individuals perform cognitive tasks such as writing, programming, analysis, and mu…
GoodPoint: Learning Constructive Scientific Paper Feedback from Author Responses
Jimin Mun, Chani Jung, Xuhui Zhou +2
While LLMs hold significant potential to transform scientific research, we advocate for their use to augment and empower researchers rather than to automate research without human…
Retro-Search: Exploring Untaken Paths for Deeper and Efficient Reasoning
Ximing Lu, Seungju Han, David Acuna +8
Large reasoning models exhibit remarkable reasoning capabilities via long, elaborate reasoning trajectories. Supervised fine-tuning on such reasoning traces, also known as distilla…
Hypothesis-Driven Theory-of-Mind Reasoning for Large Language Models
Hyunwoo Kim, Melanie Sclar, Tan Zhi-Xuan +5
Existing LLM reasoning methods have shown impressive capabilities across various tasks, such as solving math and coding problems. However, applying these methods to scenarios witho…
HAICOSYSTEM: An Ecosystem for Sandboxing Safety Risks in Human-AI Interactions
Xuhui Zhou, Hyunwoo Kim, Faeze Brahman +9
AI agents are increasingly autonomous in their interactions with human users and tools, leading to increased interactional safety risks. We present HAICOSYSTEM, a framework examini…
Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory
Niloofar Mireshghallah, Hyunwoo Kim, Xuhui Zhou +4
The interactive use of large language models (LLMs) in AI assistants (at work, home, etc.) introduces a new set of inference-time privacy risks: LLMs are fed different types of inf…