An Iterative Question-Guided Framework for Knowledge Base Question Answering
This work addresses the challenge of reliable multi-hop reasoning in knowledge base question answering, which is crucial for applications requiring accurate and transparent AI systems, though it appears incremental as it builds on existing methods with specific enhancements.
The paper tackled the problem of factual inconsistencies in knowledge-intensive question answering by introducing iQUEST, a framework that iteratively decomposes complex queries and integrates graph neural networks for multi-hop reasoning, achieving consistent improvements across four benchmark datasets and four large language models.
Large Language Models (LLMs) excel in many natural language processing tasks but often exhibit factual inconsistencies in knowledge-intensive settings. Integrating external knowledge resources, particularly knowledge graphs (KGs), provides a transparent and updatable foundation for more reliable reasoning. Knowledge Base Question Answering (KBQA), which queries and reasons over KGs, is central to this effort, especially for complex, multi-hop queries. However, multi-hop reasoning poses two key challenges: (1)~maintaining coherent reasoning paths, and (2)~avoiding prematurely discarding critical multi-hop connections. To tackle these challenges, we introduce iQUEST, a question-guided KBQA framework that iteratively decomposes complex queries into simpler sub-questions, ensuring a structured and focused reasoning trajectory. Additionally, we integrate a Graph Neural Network (GNN) to look ahead and incorporate 2-hop neighbor information at each reasoning step. This dual approach strengthens the reasoning process, enabling the model to explore viable paths more effectively. Detailed experiments demonstrate the consistent improvement delivered by iQUEST across four benchmark datasets and four LLMs.