8 papers
The Mirrored Influence Hypothesis: Efficient Data Influence Estimation by Harnessing Forward Passes
Myeongseob Ko, Feiyang Kang, Weiyan Shi +3
Large-scale black-box models have become ubiquitous across numerous applications. Understanding the influence of individual training data sources on predictions made by these model…
From Weak Cues to Real Identities: Evaluating Inference-Driven De-Anonymization in LLM Agents
Myeongseob Ko, Jihyun Jeong, Sumiran Singh Thakur +2
Anonymization is often assumed to protect privacy once explicit identifiers are removed, because re-identification has historically required specialized expertise, tailored algorit…
Injecting Measurement Information Yields a Fast and Noise-Robust Diffusion-Based Inverse Problem Solver
Jonathan Patsenker, Henry Li, Myeongseob Ko +2
Diffusion models have been firmly established as principled zero-shot solvers for linear and nonlinear inverse problems, owing to their powerful image prior and iterative sampling…
Characterizing Model-Native Skills
Feiyang Kang, Mahavir Dabas, Myeongseob Ko +1
Skills are a natural unit for describing what a language model can do and how its behavior can be changed. However, existing characterizations rely on human-written taxonomies, tex…
The Signal is in the Steps: Local Scoring for Reasoning Data Selection
Hoang Anh Just, Myeongseob Ko, Ruoxi Jia
Distilling long-form reasoning from teacher models into smaller students requires selecting which candidate solutions to train on. Recent work argues that one should select respons…
Retracing the Past: LLMs Emit Training Data When They Get Lost
Myeongseob Ko, Nikhil Reddy Billa, Adam Nguyen +3
The memorization of training data in large language models (LLMs) poses significant privacy and copyright concerns. Existing data extraction methods, particularly heuristic-based d…