9 papers · 1 filter
AMATA: Adaptive Multi-Agent Trajectory Alignment for Knowledge-Intensive Question Answering
Taolin Zhang, Dongyang Li, Chen Chen +5
Despite substantial advances in large language models (LLMs), generating factually consistent responses for knowledge-intensive question answering remains challenging. These diffic…
QueueEDIT: Structural Self-Correction for Sequential Model Editing in LLMs
Taolin Zhang, Haidong Kang, Dongyang Li +3
Recently, large language models (LLMs) have demonstrated impressive results but still suffer from hallucinations. Model editing has been proposed to correct factual inaccuracies in…
BELLE: A Bi-Level Multi-Agent Reasoning Framework for Multi-Hop Question Answering
Taolin Zhang, Dongyang Li, Qizhou Chen +2
Multi-hop question answering (QA) involves finding multiple relevant passages and performing step-by-step reasoning to answer complex questions. Previous works on multi-hop QA empl…
Lifelong Knowledge Editing for LLMs with Retrieval-Augmented Continuous Prompt Learning
Qizhou Chen, Taolin Zhang, Xiaofeng He +4
Model editing aims to correct outdated or erroneous knowledge in large language models (LLMs) without the need for costly retraining. Lifelong model editing is the most challenging…
Concept Based Continuous Prompts for Interpretable Text Classification
Qian Chen, Dongyang Li, Xiaofeng He
Continuous prompts have become widely adopted for augmenting performance across a wide range of natural language tasks. However, the underlying mechanism of this enhancement remain…
R4: Reinforced Retriever-Reorder-Responder for Retrieval-Augmented Large Language Models
Taolin Zhang, Dongyang Li, Qizhou Chen +5
Retrieval-augmented large language models (LLMs) leverage relevant content retrieved by information retrieval systems to generate correct responses, aiming to alleviate the halluci…