1 citations · 1 across the 7 of their papers we have counts for
4 papers · 1 filter
MARCH: Multi-Agent Reinforced Self-Check for LLM Hallucination
Zhuo Li, Yupeng Zhang, Pengyu Cheng +8
Hallucination remains a critical bottleneck for large language models (LLMs), undermining their reliability in real-world applications, especially in Retrieval-Augmented Generation…
Add-One-In: Incremental Sample Selection for Large Language Models via a Choice-Based Greedy Paradigm
Zhuo Li, Yuhao Du, Xiaoqi Jiao +5
Selecting high-quality and diverse training samples from extensive datasets plays a crucial role in reducing training overhead and enhancing the performance of Large Language Model…
Self-Instructed Derived Prompt Generation Meets In-Context Learning: Unlocking New Potential of Black-Box LLMs
Zhuo Li, Yuhao Du, Jinpeng Hu +2
Large language models (LLMs) have shown success in generating high-quality responses. In order to achieve better alignment with LLMs with human preference, various works are propos…
Atoxia: Red-teaming Large Language Models with Target Toxic Answers
Yuhao Du, Zhuo Li, Pengyu Cheng +2
Despite the substantial advancements in artificial intelligence, large language models (LLMs) remain being challenged by generation safety. With adversarial jailbreaking prompts, o…