Jaehun Jung

I'm a Ph.D student in computer science at the University of Washington, advised by Yejin Choi.

• Data Generation & Selection using Language Models
• Improving Reliability and Controllability of Language Models
• LLM-based Reasoning
• Behavioral Analysis of LLMs

Previously I was an undergrad at Seoul National University, advised by Professor U Kang and Jinwook Seo. I was also a part-time researcher in Kakao Enterprise, where I worked on knowledge-grounded dialogue agents.

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Research

We enhance LLM judges with a statistically rigorous guarantee of human agreement. We further extend this guarantee to propose Cascaded Selective Evaluation, where we start from a small cost-effective model as a judge, and escalate to a stronger model only when necessary—all while guaranteeing high agreement with humans.

Can small models excel at summarization without imitating LLM or human-written references? We present InfoSumm, a framework to distill a powerful summarizer that outperforms order-of-magnitude larger LLM summarizers, solely based on the information-theoretic objective for summarization.

It is possible to generate a high-quality dataset for sentential paraphrasing and summarization directly from an off-the-shelf LM, even when it is impossible for the LM itself to reliably perform these tasks.

We introduce JamDec, an inference-time algorithm for authorship obfuscation that is domain-agnostic, controllable, yet does not require human supervision.

Can we adapt LLMs without fine-tuning? We propose using a lightweight adapter (e.g. GPT-2) during decoding time, efficiently tailoring even the strongest proprietary LLMs toward user-defined reward.

We propose a text style transfer framework from arbitrary source style to many target styles via large-scale data generation with expert-guided decoding and offline RL.

We improve LM reasoning by generating abductive and recursive explanations from language models, then formulating inference as a satisfiability problem over these generations.

A novel GNN for temporal KG is proposed that encodes an interpretable graph substructure for knowledge graph completion.

We present a novel decoder model based on attention flow that learns to explore KG and retrieve a relevant knowledge path to ground a dialogue agent.

DataHalo implements a customizable notification visualization system for mobile devices, providing prolonged ambient visualizations based on time-varying importance model to enable longitudinal interaction with the notifications.

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