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

Showing 251–300 of 939 papers

TitleStatusHype
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
Holodeck: Language Guided Generation of 3D Embodied AI EnvironmentsCode2
Linguistic and Structural Basis of Engineering Design Knowledge—0
Generative agent-based modeling with actions grounded in physical, social, or digital space using ConcordiaCode3
Prompt Tuning for Zero-shot Compositional Learning—0
A framework for mining lifestyle profiles through multi-dimensional and high-order mobility feature clustering—0
Mamba: Linear-Time Sequence Modeling with Selective State SpacesCode6
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
Are Large Language Models Temporally Grounded?Code1
Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic Systems—0
Smart Agent-Based Modeling: On the Use of Large Language Models in Computer SimulationsCode1
Chain of Images for Intuitively ReasoningCode1
On the Road with GPT-4V(ision): Early Explorations of Visual-Language Model on Autonomous DrivingCode2
On the Multiple Roles of Ontologies in Explainable AI—0
NeuSyRE: Neuro-Symbolic Visual Understanding and Reasoning Framework based on Scene Graph EnrichmentCode1
COPAL-ID: Indonesian Language Reasoning with Local Culture and NuancesCode0
SAGE: Smart home Agent with Grounded Execution—0
ROME: Evaluating Pre-trained Vision-Language Models on Reasoning beyond Visual Common SenseCode0
DCQA: Document-Level Chart Question Answering towards Complex Reasoning and Common-Sense UnderstandingCode0
LLM-FP4: 4-Bit Floating-Point Quantized TransformersCode2
Exploring Large Language Models as a Source of Common-Sense Knowledge for RobotsCode0
GestureGPT: Toward Zero-Shot Free-Form Hand Gesture Understanding with Large Language Model AgentsCode0
Penetrative AI: Making LLMs Comprehend the Physical World—0
Co-NavGPT: Multi-Robot Cooperative Visual Semantic Navigation Using Vision Language Models—0
Heuristic Vision Pre-Training with Self-Supervised and Supervised Multi-Task Learning—0
NEWTON: Are Large Language Models Capable of Physical Reasoning?—0
Mistral 7BCode6
LLM-Coordination: Evaluating and Analyzing Multi-agent Coordination Abilities in Large Language ModelsCode1
AdaRefiner: Refining Decisions of Language Models with Adaptive FeedbackCode1
Telling Stories for Common Sense Zero-Shot Action RecognitionCode0
DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language ModelsCode2
Self-Refined Large Language Model as Automated Reward Function Designer for Deep Reinforcement Learning in RoboticsCode0
TrafficGPT: Viewing, Processing and Interacting with Traffic Foundation ModelsCode1
Learning to Predict Concept Ordering for Common Sense GenerationCode0
Mitigating the Alignment Tax of RLHFCode1
SAGE: Structured Attribute Value Generation for Billion-Scale Product Catalogs—0
Textbooks Are All You Need II: phi-1.5 technical report—0
What Is Near?: Room Locality Learning for Enhanced Robot Vision-Language-Navigation in Indoor Living Environments—0
SayNav: Grounding Large Language Models for Dynamic Planning to Navigation in New EnvironmentsCode1
Interpretable Visual Question Answering via Reasoning Supervision—0
Multilingual Text Representation—0
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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