用现成模型让大模型高效准确地问答知识图谱问题
UltRAG: a Universal Simple Scalable Recipe for Knowledge Graph RAG
- 用预训练查询执行模块让大模型直接操作知识图谱
- 在116M实体的维基数据上表现超越现有方法
- 无需微调模型，适合想快速接入知识图谱的开发者
大型语言模型在生成文本时常产生看似可信但事实错误的内容（即幻觉）。检索增强生成（RAG）通过从知识语料中检索信息并放入模型上下文来减少此类错误。尽管该方法在文档结构数据中已成熟应用，但将其适配至知识图谱（KG）仍具挑战性，尤其对于需要多节点/多跳推理的查询。本文提出ULTRAG，一种通用框架，使大模型能直接从知识图谱中检索信息，摆脱传统RAG范式。通过赋予大模型现成的神经查询执行模块，我们证明：无需对大模型或执行器进行任何微调，即可在知识图谱问答（KGQA）任务上达到顶尖水平。实验表明，ULTRAG性能优于当前最先进的KG-RAG方案，并可高效处理维基数据规模（11600万实体，16亿关系）的知识图谱，成本相当或更低。
原文摘要 · Abstract (English)
Large language models (LLMs) frequently generate confident yet factually incorrect content when used for language generation (a phenomenon often known as hallucination). Retrieval augmented generation (RAG) tries to reduce factual errors by identifying information in a knowledge corpus and putting it in the context window of the model. While this approach is well-established for document-structured data, it is non-trivial to adapt it for Knowledge Graphs (KGs), especially for queries that require multi-node/multi-hop reasoning on graphs. We introduce ULTRAG, a general framework for retrieving information from Knowledge Graphs that shifts away from classical RAG. By endowing LLMs with off-the-shelf neural query executing modules, we highlight how readily available language models can achieve state-of-the-art results on Knowledge Graph Question Answering (KGQA) tasks without any retraining of the LLM or executor involved. In our experiments, ULTRAG achieves better performance when compared to state-of-the-art KG-RAG solutions, and it enables language models to interface with Wikidata-scale graphs (116M entities, 1.6B relations) at comparable or lower costs.
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