Knowledge Tree
common-sense reasoning
Also known as: common sense reasoning, common-sense reasoning, commonsense reasoning
Facts (15)
Sources
A survey on augmenting knowledge graphs (KGs) with large ... link.springer.com Nov 4, 2024 3 facts
claimHellaSwag is a benchmark for evaluating commonsense reasoning in natural language by testing a model's ability to complete sentences coherently and sensibly.
claimLarge Language Models (LLMs) perform common sense reasoning and multi-task learning, allowing different tasks to be addressed within a single model.
claimBenchmarks like SimpleQuestions and FreebaseQA provide standardized datasets and evaluation metrics for consistent and comparative assessment of LLMs integrated with knowledge graphs, covering tasks such as natural language understanding, question answering, commonsense reasoning, and knowledge graph completion.
Understanding LLM Understanding skywritingspress.ca Jun 14, 2024 3 facts
claimThinking can be modelled using probabilistic programs, which provide an expressive representation for commonsense reasoning.
claimBayesian inference performed with programs generated by large language models supports coherent and robust commonsense reasoning.
claimJackie Chi Kit Cheung's research focuses on natural language generation, automatic summarization, and integrating diverse knowledge sources into NLP systems for pragmatic and common-sense reasoning.
Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org 2 facts
referenceB. Y. Lin, X. Chen, J. Chen, and X. Ren published 'Kagnet: Knowledge-aware graph networks for commonsense reasoning' as an arXiv preprint in 2019.
referenceKagNet (Lin et al., 2019) constructs pattern graphs using a knowledge-aware graph network, and resorts to graph convolutional networks, LSTM, and a hierarchical path attention mechanism to solve common-sense reasoning problems.
Unlocking the Potential of Generative AI through Neuro-Symbolic ... arxiv.org Feb 16, 2025 1 fact
claimNeural networks (NNs) struggle with reasoning and generalizing beyond their training data, particularly in tasks involving logical inference, commonsense reasoning, causality, sequential problem-solving, and decision-making that relies on outside world knowledge.
A Comprehensive Review of Neuro-symbolic AI for Robustness ... link.springer.com Dec 9, 2025 1 fact
claimThe PIGLeT model grounds language in a physical 3D environment, illustrating how neuro-symbolic systems can bridge abstract linguistic input with tangible, common-sense reasoning.
Large Language Models Meet Knowledge Graphs for Question ... arxiv.org Sep 22, 2025 1 fact
referenceCR-LT KGQA (Guo et al., 2024) is a Knowledge Graph Question Answering (KGQA) dataset that supports long-tail entities and commonsense reasoning.
KR 2026 : 23rd International Conference on Principles of ... - WikiCFP wikicfp.com 1 fact
claimThe 23rd International Conference on Principles of Knowledge Representation and Reasoning (KR 2026) covers research topics including argumentation, belief change, common-sense reasoning, computational aspects of knowledge representation, description logics, ethical considerations in knowledge representation, explanation, abduction and diagnosis, geometric, spatial, and temporal reasoning, inconsistency- and exception-tolerant reasoning, knowledge acquisition, knowledge compilation, automated reasoning, satisfiability and model counting, knowledge representation languages, logic programming, answer set programming, model learning for diagnosis and planning, modeling and reasoning about preferences, modeling constraints and constraint solving, multi- and order-sorted representations and reasoning, non-monotonic logics, ontologies and knowledge-enriched data management, philosophical foundations of knowledge representation, qualitative reasoning, reasoning about actions and change, action languages, reasoning about knowledge, beliefs, and other mental attitudes, reasoning in knowledge graphs, reasoning in multi-agent systems, semantic web, similarity-based and contextual reasoning, and uncertainty and vagueness.
Call for Papers: Main Track - KR 2026 kr.org 1 fact
claimThe KR 2026 conference accepts submissions on topics including argumentation, belief change, common-sense reasoning, computational aspects of knowledge representation, description logics, ethical considerations in KR, explanation/abduction/diagnosis, geometric/spatial/temporal reasoning, inconsistency- and exception-tolerant reasoning, knowledge acquisition, knowledge compilation/automated reasoning/satisfiability/model counting, knowledge representation languages, logic programming/answer set programming, model learning for diagnosis and planning, modeling and reasoning about preferences, modeling constraints and constraint solving, multi- and order-sorted representations and reasoning, non-monotonic logics, ontologies and knowledge-enriched data management, philosophical foundations of KR, qualitative reasoning, reasoning about actions and change/action languages, reasoning about knowledge/beliefs/mental attitudes, reasoning in knowledge graphs, reasoning in multi-agent systems, semantic web, similarity-based and contextual reasoning, and uncertainty and vagueness.
Neuro-Symbolic AI: Explainability, Challenges, and Future Trends arxiv.org Nov 7, 2024 1 fact
referenceArabshahi et al. (2021) developed a conversational neuro-symbolic system for commonsense reasoning, published in the Proceedings of the AAAI Conference on Artificial Intelligence.
Knowledge Graph Combined with Retrieval-Augmented Generation ... drpress.org Dec 2, 2025 1 fact
referenceThe paper 'Mvp-tuning: Multi-view knowledge retrieval with prompt tuning for commonsense reasoning' by Huang Y, Li Y, Xu Y, et al. was published in the Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics in 2023.