1 citations · 1 across the 4 of their papers we have counts for
7 papers
Deceptive Grounding: Entity Attribution Failure in Clinical Retrieval-Augmented Generation
Cedric Caruzzo, Donggeun Yoo, Tae Soo Kim
Retrieval-augmented generation evaluation checks whether model claims are factually grounded in retrieved documents. It does not check whether retrieved evidence is attributed to t…
DiscoverLLM: From Executing Intents to Discovering Them
Tae Soo Kim, Yoonjoo Lee, Jaesang Yu +2
To handle ambiguous and open-ended requests, Large Language Models (LLMs) are increasingly trained to interact with users to surface intents they have not yet expressed (e.g., ask…
Evalet: Evaluating Large Language Models through Functional Fragmentation
Tae Soo Kim, Heechan Lee, Yoonjoo Lee +2
Practitioners increasingly rely on Large Language Models (LLMs) to evaluate generative AI outputs through "LLM-as-a-Judge" approaches. However, these methods produce holistic score…
"When to Hand Off, When to Work Together": Expanding Human-Agent Co-Creative Collaboration through Concurrent Interaction
Kihoon Son, Hyewon Lee, DaEun Choi +6
As agents move into shared workspaces and their execution becomes visible, human-agent collaboration faces a fundamental shift from sequential delegation to concurrent co-creation.…
ClearFairy: Capturing Creative Workflows through Decision Structuring, In-Situ Questioning, and Rationale Inference
Kihoon Son, DaEun Choi, Tae Soo Kim +3
Capturing professionals' decision-making in creative workflows (e.g., UI/UX) is essential for reflection, collaboration, and knowledge sharing, yet existing methods often leave rat…
On the Regulatory Potential of User Interfaces for AI Agent Governance
K. J. Kevin Feng, Tae Soo Kim, Rock Yuren Pang +3
AI agents that take actions in their environment autonomously over extended time horizons require robust governance interventions to curb their potentially consequential risks. Pri…