5 citations · 6 across the 7 of their papers we have counts for
8 papers
Harness-Aware Distillation for Small Language Model Agents
Moonseok Choi, Taehong Moon, Giung Nam +1
Language model agents are deployed with a harness, the software around the model that manages its context, tools, and feedback. When such an agent is distilled into a smaller one,…
Verbalized Confidence Triggers Self-Verification: Emergent Behavior Without Explicit Reasoning Supervision
Chaeyun Jang, Moonseok Choi, Yegon Kim +2
Uncertainty calibration is essential for the safe deployment of large language models (LLMs), particularly when users rely on verbalized confidence estimates. While prior work has…
Safety Alignment Backfires: Preventing the Re-emergence of Suppressed Concepts in Fine-tuned Text-to-Image Diffusion Models
Sanghyun Kim, Moonseok Choi, Jinwoo Shin +1
Fine-tuning text-to-image diffusion models is widely used for personalization and adaptation for new domains. In this paper, we identify a critical vulnerability of fine-tuning: sa…
A Simple Early Exiting Framework for Accelerated Sampling in Diffusion Models
Taehong Moon, Moonseok Choi, EungGu Yun +4
Diffusion models have shown remarkable performance in generation problems over various domains including images, videos, text, and audio. A practical bottleneck of diffusion models…
Safeguard Text-to-Image Diffusion Models with Human Feedback Inversion
Sanghyun Kim, Seohyeon Jung, Balhae Kim +3
This paper addresses the societal concerns arising from large-scale text-to-image diffusion models for generating potentially harmful or copyrighted content. Existing models rely h…
Towards Safe Self-Distillation of Internet-Scale Text-to-Image Diffusion Models
Sanghyun Kim, Seohyeon Jung, Balhae Kim +3
Large-scale image generation models, with impressive quality made possible by the vast amount of data available on the Internet, raise social concerns that these models may generat…
Sparse Weight Averaging with Multiple Particles for Iterative Magnitude Pruning
Moonseok Choi, Hyungi Lee, Giung Nam +1
Given the ever-increasing size of modern neural networks, the significance of sparse architectures has surged due to their accelerated inference speeds and minimal memory demands.…
Equal Experience in Recommender Systems
Jaewoong Cho, Moonseok Choi, Changho Suh
We explore the fairness issue that arises in recommender systems. Biased data due to inherent stereotypes of particular groups (e.g., male students' average rating on mathematics i…