An Enhanced Prompt-Based LLM Reasoning Scheme via Knowledge Graph-Integrated Collaboration (2402.04978v2)

Abstract: While LLMs demonstrate exceptional performance in a multitude of NLP tasks, they encounter challenges in practical applications, including issues with hallucinations, inadequate knowledge updating, and limited transparency in the reasoning process. To overcome these limitations, this study innovatively proposes a collaborative training-free reasoning scheme involving tight cooperation between Knowledge Graph (KG) and LLMs. This scheme first involves using LLMs to iteratively explore KG, selectively retrieving a task-relevant knowledge subgraph to support reasoning. The LLMs are then guided to further combine inherent implicit knowledge to reason on the subgraph while explicitly elucidating the reasoning process. Through such a cooperative approach, our scheme achieves more reliable knowledge-based reasoning and facilitates the tracing of the reasoning results. Experimental results show that our scheme significantly progressed across multiple datasets, notably achieving over a 10% improvement on the QALD10 dataset compared to the best baseline and the fine-tuned state-of-the-art (SOTA) work. Building on this success, this study hopes to offer a valuable reference for future research in the fusion of KG and LLMs, thereby enhancing LLMs' proficiency in solving complex issues.

Enhancing LLM Reasoning through Knowledge Graph-Integrated Collaboration

Introduction

Recent advancements in LLMs have set new benchmarks across various NLP tasks. Despite these achievements, challenges such as hallucinations, timely knowledge updating, and reasoning transparency persist, undermining the practical application of LLMs. This paper presents an innovative approach to address these issues by tightly integrating Knowledge Graphs (KGs) and LLMs, facilitating enhanced reasoning capabilities and transparent output traceability.

Methodology

The cornerstone of this approach is a cooperative, training-free scheme where LLMs iteratively explore KGs, retrieving task-relevant knowledge subgraphs that then serve as a basis for reasoned output. This method involves three main phases:

1. Initialization: Identifying key entities within the input question to anchor the subsequent search in the KG.
2. Knowledge Subgraph Retrieval: Through a beam search mechanism, the method expands across relations and entities within the KG, constructing a subgraph rich in contextually relevant knowledge.
3. Reasoning: Utilizing this subgraph, the LLM then engages in a step-by-step reasoning process, where it articulates the reasoning path, leveraging its inherent knowledge alongside the explicitly derived insights from the KG.

This innovative scheme ensures the LLM's reasoning is both knowledge-based and transparent, addressing significant limitations of current models.

Experimental Setup and Results

The evaluation of this scheme was conducted over diverse datasets with tasks ranging from question answering to fact-checking, comparing baselines like the GPT-3.5-turbo with and without external knowledge integration. Notably, the scheme achieved more than a 10% improvement over the best baseline and fine-tuned state-of-the-art (SOTA) on the QALD10 dataset. Such results underscore the scheme's capability to significantly enhance LLM reasoning accuracy by incorporating external KGs.

Implications and Future Directions

The practical implications of this research are profound. By integrating KGs with LLMs, we can substantially minimize hallucinations and inaccuracies, making LLMs more reliable for critical applications in fields like medicine and finance. Theoretically, this work progresses our understanding of how external knowledge sources can be dynamically and efficiently leveraged to bolster LLM reasoning, hinting at the potential for LLMs to achieve even greater levels of comprehension and insight through external data sources.

Looking ahead, the adaptability of this scheme to various LLM and KG combinations without extra training costs suggests a broad applicability across many domains and tasks. Future developments may focus on automating the identification and integration of the most relevant KGs based on the task at hand, further enhancing the efficiency and accuracy of LLM reasoning.

Conclusion

In conclusion, this paper proposes a valuable future direction for research into the fusion of KGs and LLMs. By addressing key challenges such as factual inaccuracies and limited transparency in reasoning, the introduced scheme not only enhances the practical utility of LLMs but also contributes to the theoretical understanding of integrating structured external knowledge with generative AI models.