Monday 03 March 2025
A novel approach to multihop question answering has emerged, one that leverages graph neural networks (GNNs) to retrieve relevant sources and answer complex queries. This innovative technique, developed by a team of researchers, shows promise in tackling the challenges of open-domain question answering.
The multihop question answering problem involves retrieving multiple sources from various modalities, such as text and images, and combining this information to provide an accurate response. Traditional methods have relied on pairwise classification models, which can be computationally expensive and prone to errors. In contrast, GNNs offer a more efficient and effective solution by modeling the complex relationships between sources.
The researchers’ approach begins with the construction of a heterogeneous graph, where nodes represent sources (text passages or images) and edges signify connections between them. A star graph structure is used, where each question node is connected to all possible source nodes. This allows the GNN to learn contextual representations that capture the relationships between sources.
The GNN module is trained using pre-trained features from various modalities, such as CLIP for image embeddings and Sentence-BERT for text embeddings. These features are then fine-tuned during training to adapt to the specific question answering task. The model learns to propagate question-related information across sources, enabling it to retrieve relevant sources even when they do not contain explicit answers.
Experimental results demonstrate the effectiveness of the GNN-based approach in multihop question answering. On the WebQA dataset, the model outperforms baseline pairwise classification models by a significant margin, achieving an F1 score of 67.4% compared to 56.3%. Notably, the GNN-based approach shows particular strength in image queries, with an improvement of 4.6 points in F1 score over the baseline.
The researchers also investigated the impact of different graph structures and feature combinations on performance. They found that pruning out irrelevant source connections leads to better graph learning, while token-level cross-attention is more effective for understanding text modality sources.
This innovative approach has significant implications for natural language processing and question answering. By leveraging GNNs to model complex relationships between sources, the researchers have developed a robust and efficient method for multihop question answering. As the field continues to evolve, it will be exciting to see how this technique is applied to other domains and challenges.
The use of graph neural networks in question answering holds great promise for tackling complex queries that require information from multiple sources.
Cite this article: “Multihop Question Answering with Graph Neural Networks”, The Science Archive, 2025.
Graph Neural Networks, Multihop Question Answering, Open-Domain Question Answering, Pairwise Classification, Natural Language Processing, Question Answering, Webqa Dataset, F1 Score, Clip Embeddings, Sentence-Bert Embeddings
