33 citations · 54 across the 11 of their papers we have counts for
5 papers · 1 filter
Investigating Assistant Bias in LLM User Simulators Using a Role Vector
Daeheon Jeong, Yoonjoo Lee, Eugene Choi +2
LLM-based user simulators are increasingly used to evaluate autonomous agents at scale, in place of costly human evaluations. Despite this promise, these simulators exhibit "assist…
KnowSim: Evaluating Information Calibration in LLM Assistants with User Simulators that Learn
Yoonjoo Lee, Hyoungwook Jin, Tae Soo Kim +3
To effectively collaborate with users on knowledge-intensive tasks, Large Language Models (LLMs) must perform information calibration: matching content to a user's evolving underst…
Revibing Code from Papers: Reimplementing HCI Artifacts
Eytan Adar, Yoonjoo Lee, Nina Lei +2
Software artifacts for most technical HCI research projects are unavailable. The lack of access to these imposes limits on academic knowledge production. It is difficult to: extend…
"When to Hand Off, When to Work Together": Understanding Concurrent Human-Agent Interaction in Shared Co-Creative Workspaces
Kihoon Son, Hyewon Lee, DaEun Choi +6
Shared co-creative workspaces allow users to contribute while agents execute. Yet how users adjust their participation as their work and the agent's execution shape one another rem…
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…