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 201250 of 939 papers

TitleStatusHype
An Aposteriorical Clusterability Criterion for k-Means++ and Simplicity of Clustering0
A very preliminary analysis of DALL-E 20
Evaluating Machine Common Sense via Cloze Testing0
AgentSGEN: Multi-Agent LLM in the Loop for Semantic Collaboration and GENeration of Synthetic Data0
Debate Dynamics for Human-comprehensible Fact-checking on Knowledge Graphs0
Automatic semantic relation extraction from Portuguese texts0
EventBERT: A Pre-Trained Model for Event Correlation Reasoning0
Explanations for CommonsenseQA: New Dataset and Models0
Analogical Proportions0
Automatic Evaluation of Commonsense Knowledge for Refining Japanese ConceptNet0
Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic Systems0
CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment0
Automatic Enrichment of WordNet with Common-Sense Knowledge0
A Multimodal Social Agent0
Automatic Adaptation Rule Optimization via Large Language Models0
A Generalized Knowledge Hunting Framework for the Winograd Schema Challenge0
Empowering Autonomous Driving with Large Language Models: A Safety Perspective0
CUHK at SemEval-2020 Task 4: CommonSense Explanation, Reasoning and Prediction with Multi-task Learning0
Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense0
DAST Model: Deciding About Semantic Complexity of a Text0
DaVinci at SemEval-2024 Task 9: Few-shot prompting GPT-3.5 for Unconventional Reasoning0
Automatic Identification of Age-Appropriate Ratings of Song Lyrics0
Enabling Robots to Understand Incomplete Natural Language Instructions Using Commonsense Reasoning0
Creative Captioning: An AI Grand Challenge Based on the Dixit Board Game0
Decentralized Vehicle Coordination: The Berkeley DeepDrive Drone Dataset and Consensus-Based Models0
Automatic Text Generation by Learning from Literary Structures0
Multimodal Analysis Of Google Bard And GPT-Vision: Experiments In Visual Reasoning0
Creating 'Full-Stack' Hybrid Reasoning Systems that Prioritize and Enhance Human Intelligence0
A Vision for Semantically Enriched Data Science0
Deep Distilling: automated code generation using explainable deep learning0
AUTO-DISCERN: Autonomous Driving Using Common Sense Reasoning0
A vision-grounded dataset for predicting typical locations for verbs0
CO-STAR: Conceptualisation of Stereotypes for Analysis and Reasoning0
Deep Style Match for Complementary Recommendation0
Deep Unsupervised Hashing with Latent Semantic Components0
DEEPYANG at SemEval-2020 Task 4: Using the Hidden Layer State of BERT Model for Differentiating Common Sense0
Back to Square One: Artifact Detection, Training and Commonsense Disentanglement in the Winograd Schema0
DELTA: Decomposed Efficient Long-Term Robot Task Planning using Large Language Models0
A Unified Model for Video Understanding and Knowledge Embedding with Heterogeneous Knowledge Graph Dataset0
A Multi-Attention based Neural Network with External Knowledge for Story Ending Predicting Task0
Exploring and Analyzing Machine Commonsense Benchmarks0
Despite "super-human" performance, current LLMs are unsuited for decisions about ethics and safety0
Detect2Interact: Localizing Object Key Field in Visual Question Answering (VQA) with LLMs0
Detecting COVID-19 Conspiracy Theories with Transformers and TF-IDF0
Augmenting Autotelic Agents with Large Language Models0
Developing a concept-level knowledge base for sentiment analysis in Singlish0
Embedding Open-domain Common-sense Knowledge from Text0
DialogSum Challenge: Summarizing Real-Life Scenario Dialogues0
Augmented Translation: A New Approach to Combining Human and Machine Capabilities0
A framework for mining lifestyle profiles through multi-dimensional and high-order mobility feature clustering0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ST-MoE-32B 269B (fine-tuned)Accuracy96.1Unverified
2Unicorn 11B (fine-tuned)Accuracy91.3Unverified
3CompassMTL 567M with TailorAccuracy90.5Unverified
4CompassMTL 567MAccuracy89.6Unverified
5UnifiedQA 11B (fine-tuned)Accuracy89.4Unverified
6Claude 3 Opus (5-shot)Accuracy88.5Unverified
7GPT-4 (5-shot)Accuracy87.5Unverified
8ExDeBERTa 567MAccuracy87Unverified
9LLaMA-2 13B + MixLoRAAccuracy86.3Unverified
10LLaMA3 8B+MoSLoRAAccuracy85.8Unverified
#ModelMetricClaimedVerifiedStatus
1GPT-4 (few-shot, k=25)Accuracy96.4Unverified
2PaLM 2 (few-shot, CoT, SC)Accuracy95.1Unverified
3Shivaay (4B, few-shot, k=8)Accuracy91.04Unverified
4StupidLLMAccuracy91.03Unverified
5Claude 2 (few-shot, k=5)Accuracy91Unverified
6Claude 1.3 (few-shot, k=5)Accuracy90Unverified
7PaLM 540B (Self Improvement, Self Consistency)Accuracy89.8Unverified
8PaLM 540B (Self Consistency)Accuracy88.7Unverified
9PaLM 540B (Self Improvement, CoT Prompting)Accuracy88.3Unverified
10PaLM 540B (Self Improvement, Standard-Prompting)Accuracy87.2Unverified
#ModelMetricClaimedVerifiedStatus
1ST-MoE-32B 269B (fine-tuned)Accuracy95.2Unverified
2LLaMA 3 8B+MoSLoRA (fine-tuned)Accuracy90.5Unverified
3PaLM 2-L (1-shot)Accuracy89.7Unverified
4PaLM 2-M (1-shot)Accuracy88Unverified
5LLaMA-3 8B + MixLoRAAccuracy86.5Unverified
6Camelidae-8×34BAccuracy86.2Unverified
7PaLM 2-S (1-shot)Accuracy85.6Unverified
8LLaMA 65B + CFG (0-shot)Accuracy84.2Unverified
9GAL 120B (0-shot)Accuracy83.8Unverified
10LLaMA-2 13B + MixLoRAAccuracy83.5Unverified
#ModelMetricClaimedVerifiedStatus
1Turing NLR v5 XXL 5.4B (fine-tuned)EM95.9Unverified
2ST-MoE-32B 269B (fine-tuned)EM95.1Unverified
3T5-11BF194.1Unverified
4DeBERTa-1.5BEM94.1Unverified
5PaLM 540B (finetuned)EM94Unverified
6Vega v2 6B (fine-tuned)EM93.9Unverified
7PaLM 2-L (one-shot)F193.8Unverified
8T5-XXL 11B (fine-tuned)EM93.4Unverified
9PaLM 2-M (one-shot)F192.4Unverified
10PaLM 2-S (one-shot)F192.1Unverified