Multi-hop Reasoning and Retrieval in Embedding Space: Leveraging Large Language Models with Knowledge
arXiv:2603.13266v1 Announce Type: new Abstract: As large language models (LLMs) continue to grow in size, their abilities to tackle complex tasks have significantly improved. However, issues such as hallucination and the lack of up-to-date knowledge largely remain unresolved. Knowledge graphs (KGs), which serve as symbolic representations of real-world knowledge, offer a reliable source for enhancing reasoning. Integrating KG retrieval into LLMs can therefore strengthen their reasoning by providing dependable knowledge. Nevertheless, due to limited understanding of the underlying knowledge graph, LLMs may struggle with queries that have multiple interpretations. Additionally, the incompleteness and noise within knowledge graphs may result in retrieval failures. To address these challenges, we propose an embedding-based retrieval reasoning framework EMBRAG. In this approach, the model first generates multiple logical rules grounded in knowledge graphs based on the input query. These ru
arXiv:2603.13266v1 Announce Type: new Abstract: As large language models (LLMs) continue to grow in size, their abilities to tackle complex tasks have significantly improved. However, issues such as hallucination and the lack of up-to-date knowledge largely remain unresolved. Knowledge graphs (KGs), which serve as symbolic representations of real-world knowledge, offer a reliable source for enhancing reasoning. Integrating KG retrieval into LLMs can therefore strengthen their reasoning by providing dependable knowledge. Nevertheless, due to limited understanding of the underlying knowledge graph, LLMs may struggle with queries that have multiple interpretations. Additionally, the incompleteness and noise within knowledge graphs may result in retrieval failures. To address these challenges, we propose an embedding-based retrieval reasoning framework EMBRAG. In this approach, the model first generates multiple logical rules grounded in knowledge graphs based on the input query. These rules are then applied to reasoning in the embedding space, guided by the knowledge graph, ensuring more robust and accurate reasoning. A reranker model further interprets these rules and refines the results. Extensive experiments on two benchmark KGQA datasets demonstrate that our approach achieves the new state-of-the-art performance in KG reasoning tasks.
Executive Summary
This article proposes an embedding-based retrieval reasoning framework called EMBRAG, which integrates knowledge graphs into large language models to improve their reasoning capabilities. The framework generates multiple logical rules grounded in knowledge graphs and applies them to reasoning in the embedding space, resulting in more robust and accurate reasoning. Experimental results demonstrate state-of-the-art performance in knowledge graph question answering tasks, addressing issues such as hallucination and knowledge incompleteness.
Key Points
- ▸ Integrating knowledge graphs into large language models to enhance reasoning
- ▸ Proposing an embedding-based retrieval reasoning framework called EMBRAG
- ▸ Achieving state-of-the-art performance in knowledge graph question answering tasks
Merits
Improved Reasoning Capabilities
The framework's ability to generate multiple logical rules and apply them to reasoning in the embedding space enhances the model's reasoning capabilities.
Demerits
Potential for Noise and Incompleteness
The framework's reliance on knowledge graphs may be affected by noise and incompleteness in the graphs, potentially impacting the model's performance.
Expert Commentary
The proposed EMBRAG framework represents a significant advancement in the field of natural language processing, as it addresses the long-standing issue of hallucination in large language models. By leveraging knowledge graphs to enhance reasoning capabilities, the framework demonstrates the potential for more accurate and reliable results in knowledge graph question answering tasks. However, further research is needed to address the potential limitations of the framework, including the impact of noise and incompleteness in knowledge graphs.
Recommendations
- ✓ Future research should focus on developing more robust methods for handling noise and incompleteness in knowledge graphs
- ✓ The EMBRAG framework should be applied to a wider range of applications to demonstrate its generalizability and potential impact.