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Common Sense Reasoning

Common sense reasoning tasks are intended to require the model to go beyond pattern recognition. Instead, the model should use "common sense" or world knowledge to make inferences.

Papers

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Showing 801–850 of 939 papers

TitleStatusHype
dPASP: A Comprehensive Differentiable Probabilistic Answer Set Programming Environment For Neurosymbolic Learning and Reasoning—0
Dutch Humor Detection by Generating Negative Examples—0
Dynamic Integration of Background Knowledge in Neural NLU Systems—0
Ecological Semantics: Programming Environments for Situated Language Understanding—0
EditInspector: A Benchmark for Evaluation of Text-Guided Image Edits—0
Efficient Language Modeling with Sparse all-MLP—0
Elucidation of the Concept of Consciousness from the Theory of Non-Human Communication Agents—0
Embedding Open-domain Common-sense Knowledge from Text—0
Linguistic and Structural Basis of Engineering Design Knowledge—0
Empirically evaluating commonsense intelligence in large language models with large-scale human judgments—0
Empowering Autonomous Driving with Large Language Models: A Safety Perspective—0
Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic Systems—0
Enabling Robots to Understand Incomplete Natural Language Instructions Using Commonsense Reasoning—0
CorrespondentDream: Enhancing 3D Fidelity of Text-to-3D using Cross-View Correspondences—0
ERNIE-mmLayout: Multi-grained MultiModal Transformer for Document Understanding—0
Evaluating Machine Common Sense via Cloze Testing—0
Event2Mind: Commonsense Inference on Events, Intents, and Reactions—0
EventBERT: A Pre-Trained Model for Event Correlation Reasoning—0
Event Embeddings for Semantic Script Modeling—0
Event Sequence Model for Semantic Analysis of Time and Location in Dialogue System—0
EvoGrad: A Dynamic Take on the Winograd Schema Challenge with Human Adversaries—0
Explanations for CommonsenseQA: New Dataset and Models—0
Exploiting Pseudo Image Captions for Multimodal Summarization—0
Exploiting Proximity-Aware Tasks for Embodied Social Navigation—0
Explore before Moving: A Feasible Path Estimation and Memory Recalling Framework for Embodied Navigation—0
Can We Trust Embodied Agents? Exploring Backdoor Attacks against Embodied LLM-based Decision-Making Systems—0
Exploring Unsupervised Pretraining and Sentence Structure Modelling for Winograd Schema Challenge—0
Extended HowNet 2.0 -- An Entity-Relation Common-Sense Representation Model—0
Extracting Zero-shot Common Sense from Large Language Models for Robot 3D Scene Understanding—0
Extraction of Common-Sense Relations from Procedural Task Instructions using BERT—0
Features of Verb Complements in Co-composition: A case study of Chinese baking verb using Weibo corpus—0
FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering—0
Fine-grained evaluation of Quality Estimation for Machine translation based on a linguistically motivated Test Suite—0
FirePlace: Geometric Refinements of LLM Common Sense Reasoning for 3D Object Placement—0
Forecasting Social Navigation in Crowded Complex Scenes—0
FoundaBench: Evaluating Chinese Fundamental Knowledge Capabilities of Large Language Models—0
Fractional trends and cycles in macroeconomic time series—0
Framework for Certification of AI-Based Systems—0
Free Will Belief as a consequence of Model-based Reinforcement Learning—0
FRIDA to the Rescue! Analyzing Synthetic Data Effectiveness in Object-Based Common Sense Reasoning for Disaster Response—0
From Blind Solvers to Logical Thinkers: Benchmarking LLMs' Logical Integrity on Faulty Mathematical Problems—0
From Common Sense Reasoning to Neural Network Models through Multiple Preferences: an overview—0
Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMs—0
FusionSense: Bridging Common Sense, Vision, and Touch for Robust Sparse-View Reconstruction—0
FVQA 2.0: Introducing Adversarial Samples into Fact-based Visual Question Answering—0
FVQA: Fact-based Visual Question Answering—0
Gaze-Enabled Egocentric Video Summarization via Constrained Submodular Maximization—0
Generating Interactive Worlds with Text—0
Generating Natural Questions from Images for Multimodal Assistants—0
Generating Out-Of-Distribution Scenarios Using Language Models—0
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All datasetsWinoGrandearc_challengearc_easyReCoRDCommonsenseQAPARusRuCoSRWSDBIG-bench (Causal Judgment)BIG-bench (Date Understanding)BIG-bench (Disambiguation QA)BIG-bench (Sports Understanding)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ST-MoE-32B 269B (fine-tuned)Accuracy96.1—Unverified
2Unicorn 11B (fine-tuned)Accuracy91.3—Unverified
3CompassMTL 567M with TailorAccuracy90.5—Unverified
4CompassMTL 567MAccuracy89.6—Unverified
5UnifiedQA 11B (fine-tuned)Accuracy89.4—Unverified
6Claude 3 Opus (5-shot)Accuracy88.5—Unverified
7GPT-4 (5-shot)Accuracy87.5—Unverified
8ExDeBERTa 567MAccuracy87—Unverified
9LLaMA-2 13B + MixLoRAAccuracy86.3—Unverified
10LLaMA3 8B+MoSLoRAAccuracy85.8—Unverified
#ModelMetricClaimedVerifiedStatus
1GPT-4 (few-shot, k=25)Accuracy96.4—Unverified
2PaLM 2 (few-shot, CoT, SC)Accuracy95.1—Unverified
3Shivaay (4B, few-shot, k=8)Accuracy91.04—Unverified
4StupidLLMAccuracy91.03—Unverified
5Claude 2 (few-shot, k=5)Accuracy91—Unverified
6Claude 1.3 (few-shot, k=5)Accuracy90—Unverified
7PaLM 540B (Self Improvement, Self Consistency)Accuracy89.8—Unverified
8PaLM 540B (Self Consistency)Accuracy88.7—Unverified
9PaLM 540B (Self Improvement, CoT Prompting)Accuracy88.3—Unverified
10PaLM 540B (Self Improvement, Standard-Prompting)Accuracy87.2—Unverified
#ModelMetricClaimedVerifiedStatus
1ST-MoE-32B 269B (fine-tuned)Accuracy95.2—Unverified
2LLaMA 3 8B+MoSLoRA (fine-tuned)Accuracy90.5—Unverified
3PaLM 2-L (1-shot)Accuracy89.7—Unverified
4PaLM 2-M (1-shot)Accuracy88—Unverified
5LLaMA-3 8B + MixLoRAAccuracy86.5—Unverified
6Camelidae-8×34BAccuracy86.2—Unverified
7PaLM 2-S (1-shot)Accuracy85.6—Unverified
8LLaMA 65B + CFG (0-shot)Accuracy84.2—Unverified
9GAL 120B (0-shot)Accuracy83.8—Unverified
10LLaMA-2 13B + MixLoRAAccuracy83.5—Unverified
#ModelMetricClaimedVerifiedStatus
1Turing NLR v5 XXL 5.4B (fine-tuned)EM95.9—Unverified
2ST-MoE-32B 269B (fine-tuned)EM95.1—Unverified
3T5-11BF194.1—Unverified
4DeBERTa-1.5BEM94.1—Unverified
5PaLM 540B (finetuned)EM94—Unverified
6Vega v2 6B (fine-tuned)EM93.9—Unverified
7PaLM 2-L (one-shot)F193.8—Unverified
8T5-XXL 11B (fine-tuned)EM93.4—Unverified
9PaLM 2-M (one-shot)F192.4—Unverified
10PaLM 2-S (one-shot)F192.1—Unverified