SOTAVerified

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 201–250 of 939 papers

TitleStatusHype
Large Language Models Need Consultants for Reasoning: Becoming an Expert in a Complex Human System Through Behavior SimulationCode0
LC-LLM: Explainable Lane-Change Intention and Trajectory Predictions with Large Language Models—0
Common Sense Enhanced Knowledge-based Recommendation with Large Language ModelCode1
Grounding Language Plans in Demonstrations Through Counterfactual Perturbations—0
Hallucination Detection in Foundation Models for Decision-Making: A Flexible Definition and Review of the State of the Art—0
IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language ModelsCode1
Leveraging Large Language Model-based Room-Object Relationships Knowledge for Enhancing Multimodal-Input Object Goal Navigation—0
To Help or Not to Help: LLM-based Attentive Support for Human-Robot Group InteractionsCode1
Hierarchical Spatial Proximity Reasoning for Vision-and-Language NavigationCode0
LogicalDefender: Discovering, Extracting, and Utilizing Common-Sense Knowledge—0
PhD: A ChatGPT-Prompted Visual hallucination Evaluation DatasetCode1
Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLMCode0
Repeated Padding for Sequential RecommendationCode1
How to Understand Named Entities: Using Common Sense for News Captioning—0
ContextGPT: Infusing LLMs Knowledge into Neuro-Symbolic Activity Recognition Models—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 ModelsCode0
Multi Task Inverse Reinforcement Learning for Common Sense Reward—0
OpenFMNav: Towards Open-Set Zero-Shot Object Navigation via Vision-Language Foundation ModelsCode2
Understanding In-Context Learning with a Pelican Soup Framework—0
G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question AnsweringCode4
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 Reasoning for Deepfake DetectionCode3
Common-Sense Bias Modeling for Classification Tasks—0
HAZARD Challenge: Embodied Decision Making in Dynamically Changing EnvironmentsCode1
Knowledge Fusion of Large Language ModelsCode4
CBVS: A Large-Scale Chinese Image-Text Benchmark for Real-World Short Video Search ScenariosCode1
Large Language Models Are Neurosymbolic ReasonersCode1
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
Mixtral of ExpertsCode4
CoT-Driven Framework for Short Text Classification: Enhancing and Transferring Capabilities from Large to Smaller Model—0
Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General TasksCode2
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
A Semantic Space is Worth 256 Language Descriptions: Make Stronger Segmentation Models with Descriptive PropertiesCode1
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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