Cited by 131Logic, Reasoning, and KnowledgeBayesian Modeling and Causal InferenceAI-based Problem Solving and Planning
Neural probabilistic logic programming in DeepProbLog
Open accessManhaeve, Robin, Dumančić, Sebastijan, Kimmig, Angelika, Demeester, Thomas, De Raedt, Luc·2021·Artificial Intelligence
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Prior neuro-symbolic methods suffer from limitations such as hand-crafted or specialized module designs that are not easily generalizable, or brittleness due to the difficulty of optimization.
“Although prior works (Mao et al., 2019;Gupta et al., 2020;Manhaeve et al., 2021;Cai et al., 2021;Tian et al., 2022;Pryor et al., 2023) also propose neuro-symbolic methods to combine neural networks with symbolic reasoning, these methods suffer from limitations such as hand-crafted or specialized module designs that are not easily generalizable, or brittleness due to the difficulty of optimization.”
From Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning (2023) · cites this paper as Manhaeve et al., 2021
The Related Work section describes limitations of some prior neuro-symbolic methods, including restricted expressivity, and §7.3 identifies an approach specialized for NLP tasks. It does not establish that prior methods generally rely on hand-crafted modules that are hard to generalize or are brittle because optimization is difficult. Page numbers were not available in our copy. (LLM, conf=0.78)
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Cites
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Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning
Pan, L., Albalak, A., Wang, X., & Wang, W. Y. (2023) · arXiv · doi:10.48550/arxiv.2305.12295 · Cited by 144 · see citing works
“Although prior works (Mao et al., 2019; Gupta et al., 2020; Manhaeve et al., 2021; Cai et al., 2021; Tian et al., 2022; Pryor et al., 2022) also propose neuro-symbolic methods to combine neural networks with symbolic reasoning, these methods suffer from limitations such as hand-crafted or specialized module designs th…”
Background
- Authors
- Manhaeve, R., Dumančić, S., Kimmig, A., Demeester, T., & De Raedt, L.
- Affiliations
- KU Leuven (BE)
- Ghent University (BE)
- Corresponding author
- Manhaeve, Robin
- Venue
- Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2021-09
- Volume
- 298
- Pages
- 103504
- ISSN
- 0004-3702 (ISSN-L)
- OpenAlex
- W2975245087
- Cited by
- 131
- References
- 3
- Citations by year
- 2026: 34 · 2025: 32 · 2024: 22 · 2023: 24 · 2022: 14 · 2021: 5
- FWCI
- 5.47
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 1461 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion, other-oa)
- Lirias (KU Leuven) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- European Research Council (ERC AdG-694980)
- FWO
- Vlaamse regering
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Keywords
- probabilistic logic programming, ProbLog, program induction, probabilistic programming, learning from examples, end-to-end learning
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