核心概念
Leveraging knowledge graphs can reduce hallucinations and enhance reasoning accuracy in Large Language Models.
要約
The content explores the use of knowledge graphs to address hallucinations in Large Language Models (LLMs). It reviews various strategies for augmenting LLMs with external knowledge, focusing on reducing hallucinations. The methods are categorized into Knowledge-Aware Inference, Knowledge-Aware Learning, and Knowledge-Aware Validation. Different techniques such as KG-augmented Retrieval, Reasoning, and Generation are discussed. The effectiveness of these methods is analyzed through performance metrics like accuracy, top-k, MRR, Hits@1, and human evaluation. Future research directions include improving KG quality, MoE LLMs optimization, symbolic-subsymbolic unification, and causality-awareness integration.
統計
"Large language models operate on input text to predict the subsequent token or word in the sequence."
"Models exhibit probabilistic behavior potentially yielding varied outputs for the same input across different instances."
"Adding random information does not improve the model’s interpretation and reasoning capabilities."
"KG-augmented retrieval models enhance contextual awareness for knowledge-intensive tasks by providing relevant documents during generation."
"Fine-tuning adapts LLMs to specific domains by training them on relevant datasets."
引用
"Addressing the issue of hallucinations in these models is challenging due to their inherent probabilistic nature."
"Knowledge graph-guided entity masking schemes utilized linked knowledge graphs to mask key entities in texts."
"Retrieval augmented language model pre-training demonstrated an application in healthcare."