20 citations · 21 across the 6 of their papers we have counts for
6 papers
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…
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…