6 citations · 6 across the 35 of their papers we have counts for
4 papers · 1 filter
FANNO: Augmenting High-Quality Instruction Data with Open-Sourced LLMs Only
He Zhu, Junyou Su, Tianle Lun +4
Instruction fine-tuning stands as a crucial advancement in leveraging large language models (LLMs) for enhanced task performance. However, the annotation of instruction datasets ha…
SeqAR: Jailbreak LLMs with Sequential Auto-Generated Characters
Yan Yang, Zeguan Xiao, Xin Lu +5
The widespread applications of large language models (LLMs) have brought about concerns regarding their potential misuse. Although aligned with human preference data before release…
Unlocking Multi-View Insights in Knowledge-Dense Retrieval-Augmented Generation
Guanhua Chen, Wenhan Yu, Xiao Lu +3
While Retrieval-Augmented Generation (RAG) plays a crucial role in the application of Large Language Models (LLMs), existing retrieval methods in knowledge-dense domains like law a…
Self-DC: When to Reason and When to Act? Self Divide-and-Conquer for Compositional Unknown Questions
Hongru Wang, Boyang Xue, Baohang Zhou +5
Previous research has typically concentrated on leveraging the internal knowledge of Large Language Models (LLMs) to answer known questions (i.e., \textit{internal reasoning such a…