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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 351–400 of 939 papers

TitleStatusHype
Large Language Models Need Consultants for Reasoning: Becoming an Expert in a Complex Human System Through Behavior SimulationCode0
Hallucination Detection in Foundation Models for Decision-Making: A Flexible Definition and Review of the State of the Art—0
Grounding Language Plans in Demonstrations Through Counterfactual Perturbations—0
Leveraging Large Language Model-based Room-Object Relationships Knowledge for Enhancing Multimodal-Input Object Goal Navigation—0
LogicalDefender: Discovering, Extracting, and Utilizing Common-Sense Knowledge—0
Hierarchical Spatial Proximity Reasoning for Vision-and-Language NavigationCode0
Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM—0
ContextGPT: Infusing LLMs Knowledge into Neuro-Symbolic Activity Recognition Models—0
How to Understand Named Entities: Using Common Sense for News Captioning—0
Telecom Language Models: Must They Be Large?—0
The Claude 3 Model Family: Opus, Sonnet, Haiku—0
SERVAL: Synergy Learning between Vertical Models and LLMs towards Oracle-Level Zero-shot Medical Prediction—0
Know your exceptions: Towards an Ontology of Exceptions in Knowledge Representation—0
Commonsense Ontology Micropatterns—0
Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language ModelsCode0
RoboScript: Code Generation for Free-Form Manipulation Tasks across Real and Simulation—0
Hybrid Reasoning Based on Large Language Models for Autonomous Car DrivingCode0
EvoGrad: A Dynamic Take on the Winograd Schema Challenge with Human Adversaries—0
MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models—0
Multi Task Inverse Reinforcement Learning for Common Sense Reward—0
Understanding In-Context Learning with a Pelican Soup Framework—0
TETRIS: Towards Exploring the Robustness of Interactive Segmentation—0
Belief Scene Graphs: Expanding Partial Scenes with Objects through Computation of Expectation—0
Enhancing Cross-Modal Contextual Congruence for Crowdfunding Success using Knowledge-infused Learning—0
Vision-Language Models Provide Promptable Representations for Reinforcement Learning—0
Common-Sense Bias Modeling for Classification Tasks—0
LLMs for Relational Reasoning: How Far are We?—0
Seeing the Unseen: Visual Common Sense for Semantic Placement—0
A Content-Based Novelty Measure for Scholarly Publications: A Proof of ConceptCode0
CoT-Driven Framework for Short Text Classification: Enhancing and Transferring Capabilities from Large to Smaller Model—0
LLM-Assist: Enhancing Closed-Loop Planning with Language-Based Reasoning—0
Towards Learning Geometric Eigen-Lengths Crucial for Fitting Tasks—0
ManipLLM: Embodied Multimodal Large Language Model for Object-Centric Robotic Manipulation—0
Collaborative Synthesis of Patient Records through Multi-Visit Health State InferenceCode0
CORECODE: A Common Sense Annotated Dialogue Dataset with Benchmark Tasks for Chinese Large Language ModelsCode0
CLDR: Contrastive Learning Drug Response Models from Natural Language SupervisionCode0
Linguistic and Structural Basis of Engineering Design Knowledge—0
Prompt Tuning for Zero-shot Compositional Learning—0
A framework for mining lifestyle profiles through multi-dimensional and high-order mobility feature clustering—0
Categorical Traffic Transformer: Interpretable and Diverse Behavior Prediction with Tokenized Latent—0
Towards Top-Down Reasoning: An Explainable Multi-Agent Approach for Visual Question Answering—0
Empowering Autonomous Driving with Large Language Models: A Safety Perspective—0
RoboGPT: an intelligent agent of making embodied long-term decisions for daily instruction tasks—0
GPT-4V Takes the Wheel: Promises and Challenges for Pedestrian Behavior Prediction—0
AlignedCoT: Prompting Large Language Models via Native-Speaking DemonstrationsCode0
Orca 2: Teaching Small Language Models How to Reason—0
A Language Agent for Autonomous DrivingCode0
Investigating Data Contamination in Modern Benchmarks for Large Language Models—0
Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic Systems—0
On the Multiple Roles of Ontologies in Explainable AI—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