17 papers · 1 filter
Joint Flashback Adaptation for Forgetting-Resistant Instruction Tuning
Yukun Zhao, Lingyong Yan, Zhenyang Li +4
Large language models have achieved remarkable success in various tasks. However, it is challenging for them to learn new tasks incrementally due to catastrophic forgetting. Existi…
GRAF: Multi-turn Jailbreaking via Global Refinement and Active Fabrication
Hua Tang, Lingyong Yan, Yukun Zhao +3
Large Language Models (LLMs) have demonstrated remarkable performance across diverse tasks. Nevertheless, they still pose notable safety risks due to potential misuse for malicious…
Reasoning-to-Defend: Safety-Aware Reasoning Can Defend Large Language Models from Jailbreaking
Junda Zhu, Lingyong Yan, Shuaiqiang Wang +2
Large Reasoning Models (LRMs) have recently demonstrated impressive performances across diverse domains. However, how the safety of Large Language Models (LLMs) benefits from enhan…
MAO-ARAG: Multi-Agent Orchestration for Adaptive Retrieval-Augmented Generation
Yiqun Chen, Erhan Zhang, Lingyong Yan +4
In question-answering (QA) systems, Retrieval-Augmented Generation (RAG) has become pivotal in enhancing response accuracy and reducing hallucination issues. The architecture of RA…
Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers
Zhengliang Shi, Lingyong Yan, Dawei Yin +3
Large language models (LLMs) have been widely integrated into information retrieval to advance traditional techniques. However, effectively enabling LLMs to seek accurate knowledge…
Retrieval Models Aren't Tool-Savvy: Benchmarking Tool Retrieval for Large Language Models
Zhengliang Shi, Yuhan Wang, Lingyong Yan +4
Tool learning aims to augment large language models (LLMs) with diverse tools, enabling them to act as agents for solving practical tasks. Due to the limited context length of tool…