135 citations · 202 across the 7 of their papers we have counts for
9 papers
Ready2Blend: From Natural-Language Instructions to Composable Alignment Prompts
Jeesu Jung, Hwan Chang, Juseon Do +5
Continual alignment requires LLMs to adapt to new requirements without forgetting previously acquired behaviors. Natural-language instructions are flexible and composable but offer…
TLPO: Token-Level Policy Optimization for Mitigating Language Confusion in Large Language Models
Jinho Choo, JunSeung Lee, Jimyeong Kim +3
Large language models (LLMs) demonstrate strong multilingual capabilities, yet often fail to consistently generate responses in the intended language, exhibiting a phenomenon known…
SCALE: Upscaled Continual Learning of Large Language Models
Jin-woo Lee, Junhwa Choi, Bongkyu Hwang +8
We revisit continual pre-training for large language models and argue that progress now depends more on scaling the right structure than on scaling parameters alone. We introduce S…
Reinforcement learning to learn quantum states for Heisenberg scaling accuracy
Jeongwoo Jae, Jeonghoon Hong, Jinho Choo +1
Learning quantum states is a crucial task for realizing quantum information technology. Recently, neural approaches have emerged as promising methods for learning quantum states. W…
CAAP: Context-Aware Action Planning Prompting to Solve Computer Tasks with Front-End UI Only
Junhee Cho, Jihoon Kim, Daseul Bae +3
Software robots have long been used in Robotic Process Automation (RPA) to automate mundane and repetitive computer tasks. With the advent of Large Language Models (LLMs) and their…
Simulation-guided Beam Search for Neural Combinatorial Optimization
Jinho Choo, Yeong-Dae Kwon, Jihoon Kim +4
Neural approaches for combinatorial optimization (CO) equip a learning mechanism to discover powerful heuristics for solving complex real-world problems. While neural approaches ca…
Matrix Encoding Networks for Neural Combinatorial Optimization
Yeong-Dae Kwon, Jinho Choo, Iljoo Yoon +3
Machine Learning (ML) can help solve combinatorial optimization (CO) problems better. A popular approach is to use a neural net to compute on the parameters of a given CO problem a…
SelfMatch: Combining Contrastive Self-Supervision and Consistency for Semi-Supervised Learning
Byoungjip Kim, Jinho Choo, Yeong-Dae Kwon +3
This paper introduces SelfMatch, a semi-supervised learning method that combines the power of contrastive self-supervised learning and consistency regularization. SelfMatch consist…
POMO: Policy Optimization with Multiple Optima for Reinforcement Learning
Yeong-Dae Kwon, Jinho Choo, Byoungjip Kim +3
In neural combinatorial optimization (CO), reinforcement learning (RL) can turn a deep neural net into a fast, powerful heuristic solver of NP-hard problems. This approach has a gr…