3 papers
cs.CL2026
No Prompt Left Behind: Exploiting Zero-Variance Prompts in LLM Reinforcement Learning via Entropy-Guided Advantage Shaping
Thanh-Long V. Le, Myeongho Jeon, Kim Vu +2
Reinforcement Learning with Verifiable Rewards (RLVR) is a powerful framework for improving the reasoning abilities of Large Language Models (LLMs). However, current methods such a…
cs.HC2025
Design Opportunities for Explainable AI Paraphrasing Tools: A User Study with Non-native English Speakers
Yewon Kim, Thanh-Long V. Le, Donghwi Kim +2
We investigate how non-native English speakers (NNESs) interact with diverse information aids to assess and select AI-generated paraphrases. We develop ParaScope, an AI paraphrasin…
cs.LG2024
(FL): Overcoming Few Labels in Federated Semi-Supervised Learning
Seungjoo Lee, Thanh-Long V. Le, Jaemin Shin +1
Federated Learning (FL) is a distributed machine learning framework that trains accurate global models while preserving clients' privacy-sensitive data. However, most FL approaches…