用结构化知识图谱让大模型自己纠正事实错误
Knowledge-Aware Self-Correction in Language Models via Structured Memory Graphs
- 基于RDF三元组构建外部语义记忆图，实现无训练修正
- 在简单事实类提示上显著降低生成错误率
- 适合需要高可靠性文本生成的场景
大型语言模型虽强大，但常产生事实性错误（即幻觉）。本文提出一种轻量级、可解释的框架，通过基于RDF三元组的结构化记忆图，对大模型输出进行无需重训练或微调的后处理，利用外部语义记忆纠正事实不一致。实验使用DistilGPT-2，在简单事实类提示任务中验证了该方法的有效性。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are powerful yet prone to generating factual errors, commonly referred to as hallucinations. We present a lightweight, interpretable framework for knowledge-aware self-correction of LLM outputs using structured memory graphs based on RDF triples. Without retraining or fine-tuning, our method post-processes model outputs and corrects factual inconsistencies via external semantic memory. We demonstrate the approach using DistilGPT-2 and show promising results on simple factual prompts.
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