John Chung — accepted AI-conference papers

2026

DiscoverLLM: From Executing Intents to Discovering Them

Tae Soo Kim, Yoonjoo Lee, Jaesang Yu, John Chung, Juho Kim

ICML 2026poster

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 clarification questions). However, users are often ambiguous because they have not yet formed their intents: they must o…

Cited by 0 Source ↗Scholar ↗

2023

Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions

John Chung, Ece Kamar, Saleema Amershi

ACL 2023long

Large language models (LLMs) can be used to generate text data for training and evaluating other models. However, creating high-quality datasets with LLMs can be challenging. In this work, we explore human-AI partnerships to facilitate high diversity and accuracy in LLM-based text data generation. W…

Cited by 135 Source ↗PDF ↗Scholar ↗