8 citations · 16 across the 21 of their papers we have counts for
5 papers · 1 filter
NILE: Internal Consistency Alignment in Large Language Models
Minda Hu, Qiyuan Zhang, Yufei Wang +7
As a crucial step to enhance LLMs alignment with human intentions, Instruction Fine-Tuning (IFT) has a high demand on dataset quality. However, existing IFT datasets often contain…
Purple-teaming LLMs with Adversarial Defender Training
Jingyan Zhou, Kun Li, Junan Li +4
Existing efforts in safeguarding LLMs are limited in actively exposing the vulnerabilities of the target LLM and readily adapting to newly emerging safety risks. To address this, w…
Mitigating Large Language Model Hallucination with Faithful Finetuning
Minda Hu, Bowei He, Yufei Wang +3
Large language models (LLMs) have demonstrated remarkable performance on various natural language processing tasks. However, they are prone to generating fluent yet untruthful resp…
SeRTS: Self-Rewarding Tree Search for Biomedical Retrieval-Augmented Generation
Minda Hu, Licheng Zong, Hongru Wang +6
Large Language Models (LLMs) have shown great potential in the biomedical domain with the advancement of retrieval-augmented generation (RAG). However, existing retrieval-augmented…
RL-GPT: Integrating Reinforcement Learning and Code-as-policy
Shaoteng Liu, Haoqi Yuan, Minda Hu +5
Large Language Models (LLMs) have demonstrated proficiency in utilizing various tools by coding, yet they face limitations in handling intricate logic and precise control. In embod…