4 papers
Unlearners Can Lie: Evaluating and Improving Honesty in LLM Unlearning
Renjie Gu, Jiazhen Du, Yihua Zhang +1
Unlearning in large language models (LLMs) aims to remove harmful training data while preserving overall utility. However, we find that existing methods often hallucinate, generate…
DualRAG: A Dual-Process Approach to Integrate Reasoning and Retrieval for Multi-Hop Question Answering
Rong Cheng, Jinyi Liu, Yan Zheng +6
Multi-Hop Question Answering (MHQA) tasks permeate real-world applications, posing challenges in orchestrating multi-step reasoning across diverse knowledge domains. While existing…
Agentic Reinforced Policy Optimization
Guanting Dong, Hangyu Mao, Kai Ma +11
Large-scale reinforcement learning with verifiable rewards (RLVR) has demonstrated its effectiveness in harnessing the potential of large language models (LLMs) for single-turn rea…
DMQR-RAG: Diverse Multi-Query Rewriting for RAG
Zhicong Li, Jiahao Wang, Zhishu Jiang +7
Large language models often encounter challenges with static knowledge and hallucinations, which undermine their reliability. Retrieval-augmented generation (RAG) mitigates these i…