33 citations · 54 across the 11 of their papers we have counts for
11 papers
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…
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…
CUPID: Evaluating Personalized and Contextualized Alignment of LLMs from Interactions
Tae Soo Kim, Yoonjoo Lee, Yoonah Park +3
Personalization of Large Language Models (LLMs) often assumes users hold static preferences that reflect globally in all tasks. In reality, humans hold dynamic preferences that cha…
ArxivDIGESTables: Synthesizing Scientific Literature into Tables using Language Models
Benjamin Newman, Yoonjoo Lee, Aakanksha Naik +6
When conducting literature reviews, scientists often create literature review tables - tables whose rows are publications and whose columns constitute a schema, a set of aspects us…
One vs. Many: Comprehending Accurate Information from Multiple Erroneous and Inconsistent AI Generations
Yoonjoo Lee, Kihoon Son, Tae Soo Kim +4
As Large Language Models (LLMs) are nondeterministic, the same input can generate different outputs, some of which may be incorrect or hallucinated. If run again, the LLM may corre…
VIVID: Human-AI Collaborative Authoring of Vicarious Dialogues from Lecture Videos
Seulgi Choi, Hyewon Lee, Yoonjoo Lee +1
The lengthy monologue-style online lectures cause learners to lose engagement easily. Designing lectures in a "vicarious dialogue" format can foster learners' cognitive activities…
PaperWeaver: Enriching Topical Paper Alerts by Contextualizing Recommended Papers with User-collected Papers
Yoonjoo Lee, Hyeonsu B. Kang, Matt Latzke +4
With the rapid growth of scholarly archives, researchers subscribe to "paper alert" systems that periodically provide them with recommendations of recently published papers that ar…