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 351–400 of 939 papers

TitleStatusHype
A very preliminary analysis of DALL-E 2—0
An Aposteriorical Clusterability Criterion for k-Means++ and Simplicity of Clustering—0
Decentralized Vehicle Coordination: The Berkeley DeepDrive Drone Dataset and Consensus-Based Models—0
Automatic Text Generation by Learning from Literary Structures—0
Debate Dynamics for Human-comprehensible Fact-checking on Knowledge Graphs—0
Automatic semantic relation extraction from Portuguese texts—0
AgentSGEN: Multi-Agent LLM in the Loop for Semantic Collaboration and GENeration of Synthetic Data—0
Abductive Reasoning as Self-Supervision for Common Sense Question Answering—0
LLM-Advisor: An LLM Benchmark for Cost-efficient Path Planning across Multiple Terrains—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
DAST Model: Deciding About Semantic Complexity of a Text—0
Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense—0
Automatic Evaluation of Commonsense Knowledge for Refining Japanese ConceptNet—0
Analogical Proportions—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
Automatic Adaptation Rule Optimization via Large Language Models—0
A Multimodal Social Agent—0
A Generalized Knowledge Hunting Framework for the Winograd Schema Challenge—0
Creative Captioning: An AI Grand Challenge Based on the Dixit Board Game—0
Creating 'Full-Stack' Hybrid Reasoning Systems that Prioritize and Enhance Human Intelligence—0
AUTO-DISCERN: Autonomous Driving Using Common Sense Reasoning—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
Augmenting Autotelic Agents with Large Language Models—0
Augmented Translation: A New Approach to Combining Human and Machine Capabilities—0
A Multi-Attention based Neural Network with External Knowledge for Story Ending Predicting Task—0
A framework for mining lifestyle profiles through multi-dimensional and high-order mobility feature clustering—0
Cooperating with Machines—0
Audit-LLM: Multi-Agent Collaboration for Log-based Insider Threat Detection—0
Conversational Multi-Hop Reasoning with Neural Commonsense Knowledge and Symbolic Logic Rules—0
Stereotype Detection in LLMs: A Multiclass, Explainable, and Benchmark-Driven Approach—0
Ambiguss, a game for building a Sense Annotated Corpus for French—0
Conversational AI : Open Domain Question Answering and Commonsense Reasoning—0
Converging Measures and an Emergent Model: A Meta-Analysis of Human-Automation Trust Questionnaires—0
Attentioned Convolutional LSTM InpaintingNetwork for Anomaly Detection in Videos—0
ContextGPT: Infusing LLMs Knowledge into Neuro-Symbolic Activity Recognition Models—0
A Tool for Extracting Conversational Implicatures—0
A mathematical theory of super-resolution and two-point resolution—0
Affordance Extraction and Inference based on Semantic Role Labeling—0
Human-Object Interaction from Human-Level Instructions—0
Context-based Natural Language Processing for GIS-based Vague Region Visualization—0
HR@JUST Team at SemEval-2020 Task 4: The Impact of RoBERTa Transformer for Evaluation Common Sense Understanding—0
How to Understand Named Entities: Using Common Sense for News Captioning—0
Content selection as semantic-based ontology exploration—0
ATLAS: Learning to Optimally Memorize the Context at Test Time—0
"Tidy Up the Table": Grounding Common-sense Objective for Tabletop Object Rearrangement—0
How Pre-trained Word Representations Capture Commonsense Physical Comparisons—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