3 papers
cs.CL2025
Reasoning Models Hallucinate More: Factuality-Aware Reinforcement Learning for Large Reasoning Models
Junyi Li, Hwee Tou Ng
Large language models (LLMs) have significantly advanced in reasoning tasks through reinforcement learning (RL) optimization, achieving impressive capabilities across various chall…
cs.CL2025
Think&Cite: Improving Attributed Text Generation with Self-Guided Tree Search and Progress Reward Modeling
Junyi Li, Hwee Tou Ng
Despite their outstanding capabilities, large language models (LLMs) are prone to hallucination and producing factually incorrect information. This challenge has spurred efforts in…
cs.CL2025
Just Go Parallel: Improving the Multilingual Capabilities of Large Language Models
Muhammad Reza Qorib, Junyi Li, Hwee Tou Ng
Large language models (LLMs) have demonstrated impressive translation capabilities even without being explicitly trained on parallel data. This remarkable property has led some to…