用大模型自动修复图数据错误，提升准确率与效率。
Graph Repairs with Large Language Models: An Empirical Study
- 利用大模型上下文理解能力实现图数据自动纠错
- 六种开源模型表现不一，修复准确率差异显著
- 适合需要自动化修复的医疗、金融等大规模图数据场景
属性图广泛应用于医疗、金融和社交网络等领域，但常因不一致、缺失数据或模式违反而存在错误。传统基于规则和启发式的修复方法适应性差，需为每组数据定制；而依赖人工介入的方法在处理大规模图时成本过高。大语言模型（LLMs）凭借其上下文推理能力和对现实世界知识的访问，为自动化图修复提供了新可能。我们评估了六种开源LLM在修复属性图方面的效果，涵盖修复质量、计算开销和模型特异性表现。实验表明，LLMs具备检测并修正错误的潜力，但准确率与效率因模型而异。本文讨论了其优势、局限与挑战，并提出未来在可扩展性和可解释性方面改进的研究方向。
原文摘要 · Abstract (English)
Property graphs are widely used in domains such as healthcare, finance, and social networks, but they often contain errors due to inconsistencies, missing data, or schema violations. Traditional rule-based and heuristic-driven graph repair methods are limited in their adaptability as they need to be tailored for each dataset. On the other hand, interactive human-in-the-loop approaches may become infeasible when dealing with large graphs, as the cost--both in terms of time and effort--of involving users becomes too high. Recent advancements in Large Language Models (LLMs) present new opportunities for automated graph repair by leveraging contextual reasoning and their access to real-world knowledge. We evaluate the effectiveness of six open-source LLMs in repairing property graphs. We assess repair quality, computational cost, and model-specific performance. Our experiments show that LLMs have the potential to detect and correct errors, with varying degrees of accuracy and efficiency. We discuss the strengths, limitations, and challenges of LLM-driven graph repair and outline future research directions for improving scalability and interpretability.
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