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Found 4 papers, 2 papers with code
Explicit Graph Reasoning Fusing Knowledge and Contextual Information for Multi-hop Question Answering
Current graph-neural-network-based (GNN-based) approaches to multi-hop questions integrate clues from scattered paragraphs in an entity graph, achieving implicit reasoning by synchronous update of graph node representations using information from neighbours; this is poorly suited for explaining how clues are passed through the graph in hops.
Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning
Combining large language models with logical reasoning enhances their capacity to address problems in a robust and reliable manner.
Prompt-based Conservation Learning for Multi-hop Question Answering
In this paper, we propose the Prompt-based Conservation Learning (PCL) framework for multi-hop QA, which acquires new knowledge from multi-hop QA tasks while conserving old knowledge learned on single-hop QA tasks, mitigating forgetting.
Interpretable AMR-Based Question Decomposition for Multi-hop Question Answering
We then achieve the decomposition of a multi-hop question via segmentation of the corresponding AMR graph based on the required reasoning type.
