Large Language Models (LLMs), such as the GPT series, have achieved remarkable performance in question answering through large-scale pretraining. However, LLMs often lack transparency in their reasoning processes and struggle with hallucination. To overcome these challenges, we propose Dual-NeSy, a Dual-system framework that integrates Neural networks with Symbolic logic for interpretable question answering. Specifically, Dual-NeSy leverages fast, heuristic reasoning for knowledge composition (System 1) and utilizes structured, multi-step symbolic reasoning (System 2) for more deliberate, logical verification. This combined approach enhances both the interpretability and accuracy of reasoning. Our approach outperforms previous methods in commonsense reasoning and reading comprehension tasks, achieving state-of-the-art results on three benchmark datasets: QASC, WorldTree, and WikiHop.

202501 bcrc

Many question-answering problems can be approached as textual entailment tasks, where the hypotheses are formed by the question and candidate answers, and the premises are derived from an external knowledge base. However, current neural methods often lack transparency in their decision-making processes. Moreover, first-order logic methods, while systematic, struggle to integrate unstructured external knowledge. To address these limitations, we propose a neuro-symbolic reasoning framework calledFinal, which combinesFIrst-order logic withNAturalLogic for question answering. Our framework utilizesfirst-order logicto systematically decompose hypotheses andnatural logicto construct reasoning paths from premises to hypotheses, employing bidirectional reasoning to establish links along the reasoning path. This approach not only enhances interpretability but also effectively integrates unstructured knowledge. Our experiments on three benchmark datasets, namely QASC, WorldTree, and WikiHop, demonstrate thatFinaloutperforms existing methods in commonsense reasoning and reading comprehension tasks, achieving state-of-the-art results. Additionally, our framework also provides transparent reasoning paths that elucidate the rationale behind the correct decisions.

Deep learning has been widely applied to automated leakage detection and location of natural gas pipe networks. Prevalent deep learning approaches do not consider the spatial dependency of sensors, which limits leakage detection performance. Graph deep learning is a promising alternative to prevailing approaches as it can model spatial dependency . However, the challenge of collecting real-world anomaly data for training limits the accuracy and robustness of currently used graph deep learning approaches. This study proposes a deep probabilistic graph neural network in which attention-based graph neural network is built to model spatial sensor dependency. Variational Bayesian inference is integrated to model the posterior distribution of sensor dependency so that the leakage can be localized. An urban natural gas pipe network experiment is employed to construct the benchmark dataset, in which normal time-series data is applied to develop our proposed model while anomaly leakage data is used for performance comparison between our model and other state-of-the-art models. The results demonstrate that our model exhibits competitive detection accuracy (AUC) = 0.9484, while the additional uncertainty interval provides more comprehensive leakage detection information compared to state-of-the-art deep learning models. In addition, our model’s posterior distribution enhances the leakage localization with the accuracy of positioning (PAc) = 0.8, which is higher than that of other state-of-the-art graph deep learning models. This study provides a comprehensive and robust alternative for subsequent decision-making to mitigate natural gas leakage from pipe networks.

Many open-domain question answering problems can be cast as a textual entailment task, where a question and candidate answers are concatenated to form hypotheses.A QA system then determines if the supporting knowledge bases, regarded as potential premises, entail the hypotheses.In this paper, we investigate a neural-symbolic QA approach that integrates natural logic reasoning within deep learning architectures, towards developing effective and yet explainable question answering models.The proposed model gradually bridges a hypothesis and candidate premises following natural logic inference steps to build proof paths.Entailment scores between the acquired intermediate hypotheses and candidate premises are measured to determine if a premise entails the hypothesis.As the natural logic reasoning process forms a tree-like, hierarchical structure, we embed hypotheses and premises in a Hyperbolic space rather than Euclidean space to acquire more precise representations.Empirically, our method outperforms prior work on answering multiple-choice science questions, achieving the best results on two publicly available datasets.The natural logic inference process inherently provides evidence to help explain the prediction process.