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 201–250 of 939 papers

TitleStatusHype
An Aposteriorical Clusterability Criterion for k-Means++ and Simplicity of Clustering—0
A very preliminary analysis of DALL-E 2—0
Enabling Robots to Understand Incomplete Natural Language Instructions Using Commonsense Reasoning—0
AgentSGEN: Multi-Agent LLM in the Loop for Semantic Collaboration and GENeration of Synthetic Data—0
Debate Dynamics for Human-comprehensible Fact-checking on Knowledge Graphs—0
Automatic semantic relation extraction from Portuguese texts—0
Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic Systems—0
CorrespondentDream: Enhancing 3D Fidelity of Text-to-3D using Cross-View Correspondences—0
Evaluating Machine Common Sense via Cloze Testing—0
Analogical Proportions—0
Automatic Evaluation of Commonsense Knowledge for Refining Japanese ConceptNet—0
Embedding Open-domain Common-sense Knowledge from Text—0
CUHK at SemEval-2020 Task 4: CommonSense Explanation, Reasoning and Prediction with Multi-task Learning—0
CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment—0
Automatic Enrichment of WordNet with Common-Sense Knowledge—0
A Multimodal Social Agent—0
Automatic Adaptation Rule Optimization via Large Language Models—0
A Generalized Knowledge Hunting Framework for the Winograd Schema Challenge—0
Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense—0
DAST Model: Deciding About Semantic Complexity of a Text—0
DaVinci at SemEval-2024 Task 9: Few-shot prompting GPT-3.5 for Unconventional Reasoning—0
Automatic Identification of Age-Appropriate Ratings of Song Lyrics—0
Elucidation of the Concept of Consciousness from the Theory of Non-Human Communication Agents—0
Linguistic and Structural Basis of Engineering Design Knowledge—0
Decentralized Vehicle Coordination: The Berkeley DeepDrive Drone Dataset and Consensus-Based Models—0
Automatic Text Generation by Learning from Literary Structures—0
Multimodal Analysis Of Google Bard And GPT-Vision: Experiments In Visual Reasoning—0
Creative Captioning: An AI Grand Challenge Based on the Dixit Board Game—0
A Vision for Semantically Enriched Data Science—0
Deep Distilling: automated code generation using explainable deep learning—0
Creating 'Full-Stack' Hybrid Reasoning Systems that Prioritize and Enhance Human Intelligence—0
A vision-grounded dataset for predicting typical locations for verbs—0
AUTO-DISCERN: Autonomous Driving Using Common Sense Reasoning—0
Deep Style Match for Complementary Recommendation—0
Deep Unsupervised Hashing with Latent Semantic Components—0
DEEPYANG at SemEval-2020 Task 4: Using the Hidden Layer State of BERT Model for Differentiating Common Sense—0
Back to Square One: Artifact Detection, Training and Commonsense Disentanglement in the Winograd Schema—0
DELTA: Decomposed Efficient Long-Term Robot Task Planning using Large Language Models—0
CO-STAR: Conceptualisation of Stereotypes for Analysis and Reasoning—0
A Unified Model for Video Understanding and Knowledge Embedding with Heterogeneous Knowledge Graph Dataset—0
A Multi-Attention based Neural Network with External Knowledge for Story Ending Predicting Task—0
Despite "super-human" performance, current LLMs are unsuited for decisions about ethics and safety—0
Exploring and Analyzing Machine Commonsense Benchmarks—0
Detecting COVID-19 Conspiracy Theories with Transformers and TF-IDF—0
LLM-Advisor: An LLM Benchmark for Cost-efficient Path Planning across Multiple Terrains—0
Developing a concept-level knowledge base for sentiment analysis in Singlish—0
Augmenting Autotelic Agents with Large Language Models—0
DialogSum Challenge: Summarizing Real-Life Scenario Dialogues—0
Ecological Semantics: Programming Environments for Situated Language Understanding—0
Augmented Translation: A New Approach to Combining Human and Machine Capabilities—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