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  • [2504. 07640] Enhancing Large Language Models through Neuro-Symbolic . . .
    We propose a neuro-symbolic approach integrating symbolic ontological reasoning and machine learning methods to enhance the consistency and reliability of LLM outputs Our workflow utilizes OWL ontologies, a symbolic reasoner (e g , HermiT) for consistency checking, and a lightweight machine learning model (logistic regression) for mapping
  • About Me - Xiao-Wen Yang
    Neural Symbolic Integration: integrating the power of machine learning and logical reasoning to boost the reasoning and planning capabilities of the AI system; News 🎉 [Jan 2025] CARTS is accepted by ICLR2025 🤖 [Feb 2025] Our paper “Step Back to Leap Forward: Self-Backtracking for Boosting Reasoning of Language Models” is available
  • LAMDASZ-ML Awesome-LLM-Reasoning-with-NeSy - GitHub
    Curated collection of papers and resources on latest advances on improving reasoning and planning abilities of LLM MLLMs with neuro-symbolic learning What's Left? Concept Grounding with Logic-Enhanced Foundation Models ↑ Back to Top ↑ Uh oh! There was an error while loading Please reload this page
  • New methods boost reasoning in large language models
    Artificial intelligence is advancing across a wide range of fields, with one of the most important developments being its growing capacity for reasoning Figure 4 An overview of the neuro-symbolic data generation framework Chain-of-Reasoning: Towards Unified Mathematical Reasoning in Large Language Models via a Multi-Paradigm Perspective
  • A review of Neuro-Symbolic AI integrating reasoning and learning for . . .
    Neuro-symbolic AI enhances NLP systems by including symbolic reasoning, enabling models to utilize logical principles in text interpretation and improving performance in tasks such as machine translation, semantic parsing, and automated theorem proving (Bhuyan et al (2024))
  • Neuro-Symbolic AI: Integrating Symbolic Reasoning with Deep Learning
    Abstract: Neuro-symbolic artificial intelligence (AI) stands at the frontier of machine learning by amalgamating the interpretability and structured knowledge representation of symbolic reasoning with the adaptive learning capabilities of deep neural networks
  • Is neuro-symbolic AI meeting its promises in natural language . . .
    We conduct a structured review of studies implementing NeSy for NLP, with the aim of answering the question of whether NeSy is indeed meeting its promises: reasoning, out-of-distribution generalization, interpretability, learning and reasoning from small data, and transferability to new domains
  • The Emergence of Neuro-Symbolic Artificial Intelligence
    By integrating the rule-based knowledge representation of symbolic AI with the learning capabilities of neural networks, Neuro-Symbolic AI aims to create systems that can understand and learn from their environment while explaining their decision-making processes in a manner comprehensible to humans
  • Neuro-Symbolic AI Explained: The Future of Smarter AI 2025
    Discover how neuro-symbolic AI merges deep learning with symbolic reasoning to build more intelligent, explainable, and human-like AI systems
  • Unlocking the Potential of Generative AI through Neuro-Symbolic . . .
    Neuro-symbolic artificial intelligence (NSAI) represents a transformative approach in artificial intelligence (AI) by combining deep learning’s ability to handle large-scale and unstructured data with the structured reasoning of symbolic methods





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