Diff-Explainer: Differentiable Convex Optimization for Explainable Multi-hop Inference (2022.tacl-1)
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| Challenge: | Existing explainable multi-hop inference models are regarded as black-boxes due to their ability to transfer linguistic and semantic information to downstream tasks, posing concerns about interpretability and transparency of their predictions. |
| Approach: | They propose a hybrid framework that integrates explicit constraints with neural architectures through differentiable convex optimization to answer and explain multi-hop questions in natural language. |
| Outcome: | The proposed framework improves performance on scientific and commonsense QA tasks while still providing structured explanations in support of its predictions. |
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Explainable Question Answering based on Semantic Graph by Global Differentiable Learning and Dynamic Adaptive Reasoning (2022.emnlp-main)
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| Challenge: | Existing models for multi-hop Question Answering have improved the implicit reasoning ability, but the black box nature of pure neural networks has hindered the construction of explainable intelligent systems. |
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Summarize-then-Answer: Generating Concise Explanations for Multi-hop Reading Comprehension (2021.emnlp-main)
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Locate Then Ask: Interpretable Stepwise Reasoning for Multi-hop Question Answering (2022.coling-1)
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Unification-based Reconstruction of Multi-hop Explanations for Science Questions (2021.eacl-main)
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| Challenge: | Existing approaches build explanations considering each question in isolation, but new approach leverages explanatory patterns emerging in scientific explanations. |
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Hop, Union, Generate: Explainable Multi-hop Reasoning without Rationale Supervision (2023.emnlp-main)
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| Challenge: | Existing methods rely on supervision for both answers and rationales, but they have limited capacities in modeling interactions between sentences, let alone reasoning across multiple documents. |
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Modeling Multi-hop Question Answering as Single Sequence Prediction (2022.acl-long)
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| Challenge: | Existing generative question answering models that leverage passage retrieval with a pre-trained transformer are not effective for multihop QA. |
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Calibrating Trust of Multi-Hop Question Answering Systems with Decompositional Probes (2022.findings-emnlp)
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Multi-Hop Reasoning for Question Answering with Hyperbolic Representations (2025.findings-acl)
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| Challenge: | a rigorous and detailed comparison of the two spaces for multi-hop reasoning is lacking. |
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Explainable Inference Over Grounding-Abstract Chains for Science Questions (2021.findings-acl)
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| Challenge: | Existing inference models for science questions are black-box by nature, lacking explanations for their predictions. |
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