Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering (2005.00646v2)

Abstract: Existing work on augmenting question answering (QA) models with external knowledge (e.g., knowledge graphs) either struggle to model multi-hop relations efficiently, or lack transparency into the model's prediction rationale. In this paper, we propose a novel knowledge-aware approach that equips pre-trained LLMs (PTLMs) with a multi-hop relational reasoning module, named multi-hop graph relation network (MHGRN). It performs multi-hop, multi-relational reasoning over subgraphs extracted from external knowledge graphs. The proposed reasoning module unifies path-based reasoning methods and graph neural networks to achieve better interpretability and scalability. We also empirically show its effectiveness and scalability on CommonsenseQA and OpenbookQA datasets, and interpret its behaviors with case studies.

Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering

The paper presents a novel approach to augmenting pre-trained LLMs (PTLMs) with a structured reasoning module designed to enhance multi-hop relational reasoning for knowledge-aware question answering (QA) tasks. Notably, the paper critiques the shortcomings of existing QA models that utilize external knowledge sources, such as knowledge graphs (KGs), in terms of either efficiency or interpretability.

Overview of Multi-Hop Graph Relation Network (MHGRN)

The authors introduce the Multi-Hop Graph Relation Network (MHGRN), a scalable and interpretable model that combines the advantages of path-based reasoning methods and graph neural networks (GNNs). MHGRN performs multi-hop, multi-relational reasoning over subgraphs extracted from external KGs, aiming at generating better-informed and interpretable predictions.

Key Features of MHGRN

1. Unified Graph Encoding: MHGRN effectively integrates the approaches of GNNs and path-based models by preserving message passing formulation, hence allowing each node in the graph to directly attend to its multi-hop neighbors.
2. Structured Relational Attention: This component is crucial for MHGRN's interpretability. It models relational paths explicitly using a structured attention mechanism that effectively weighs different relations and paths, thereby elucidating the reasoning process taken by the model.
3. Scalability: The algorithm is designed to handle denser graphs efficiently, with computation complexity largely based on the sparsity of the input graph, making it viable for large-scale applications.
4. Interpretability: The model’s architecture supports the decoding of relational paths that can serve as transparent evidence explaining the decisions made by the QA system.

Empirical Validations

The effectiveness of MHGRN was evaluated on the CommonsenseQA and OpenbookQA datasets. The results demonstrated significant improvements over baseline models relying solely on PTLMs. Specifically, MHGRN exhibited substantial performance gains, achieving higher accuracies than comparable models that also leverage external knowledge graphs.

Implications and Future Work

The introduction of MHGRN represents a significant advancement in the integration of structured knowledge bases with PTLMs. The enhancements in scalability and interpretability suggest that MHGRN could play a pivotal role in developing more trustworthy AI systems that require explicit reasoning capabilities. In terms of future research directions, the model's ability to integrate with various kinds of external knowledge sources and its application across different domains remains an open and promising field of paper.

Additionally, improvements could be made in terms of reducing computational resource requirements or applying the model to other AI tasks that benefit from structured reasoning, such as natural language inference or dialogue systems. Given these prospects, MHGRN positions itself as a flexible and practical tool for future developments in AI-driven knowledge reasoning systems.