2 papers
cs.CL2025
TruthRL: Incentivizing Truthful LLMs via Reinforcement Learning
Zhepei Wei, Xiao Yang, Kai Sun +12
While large language models (LLMs) have demonstrated strong performance on factoid question answering, they are still prone to hallucination and untruthful responses, particularly…
cs.AI2025
Refine-n-Judge: Curating High-Quality Preference Chains for LLM-Fine-Tuning
Derin Cayir, Renjie Tao, Rashi Rungta +6
Large Language Models (LLMs) have demonstrated remarkable progress through preference-based fine-tuning, which critically depends on the quality of the underlying training data. Wh…