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Neurosymbolic Methods
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Neurosymbolic Methods

Overviews

Papers

Neural Module Networks
Murray & Krishnamurthy 2016 - Probabilistic Neural Programs
Hu et al 2016 - Harnessing Deep Neural Networks with Logic Rules
Evans & Grefenstette 2017 - Learning Explanatory Rules from Noisy Data Introduces Differentiable Inductive Logic Programming, which is trained using backpropagation. DILP provides “data efficiency and generalisation beyond what neural networks can achieve on their own.”
Evans et al 2018 - Can Neural Networks Understand Logical Entailment?
Wang & Poon 2018 - Deep Probabilistic Logic: A Unifying Framework for Indirect Supervision (summary here, from Wang 2019)
Wang et al 2019 - Evidence Sentence Extraction for Machine Reading Comprehension Uses Deep Probabilistic Logic
Cohen et al 2020 - Tensorlog: A probabilistic database implemented using deep-learning infrastructure
Wu et al 2020 - Deep Weighted MaxSAT for Aspect-based Opinion Extraction See the related work
Feng et al 2022 - Neuro-symbolic Natural Logic with Introspective Revision for Natural Language Inference
Qian et al 2022 - Limitations of Language Models in Arithmetic and Symbolic Induction
West et al 2022 - Symbolic Knowledge Distillation: from General Language Models to Commonsense Models
Gupta & Kembhavi 2022 - Visual Programming: Compositional visual reasoning without training
Pan et al 2023 - Fact-Checking Complex Claims with Program-Guided Reasoning
Pan et al 2023 - Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning
Wang & Shu 2023 - Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Models
Hsu et al 2023 - What’s Left? Concept Grounding with Logic-Enhanced Foundation Models
Liu et al 2025 - Safe: Enhancing Mathematical Reasoning in Large Language Models via Retrospective Step-aware Formal Verification

People

William W. Cohen

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