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 151–200 of 939 papers

TitleStatusHype
Conversational Word Embedding for Retrieval-Based Dialog SystemCode1
Grounding Consistency: Distilling Spatial Common Sense for Precise Visual Relationship DetectionCode1
Evaluation Toolkit For Robustness Testing Of Automatic Essay Scoring SystemsCode1
Guiding Pretraining in Reinforcement Learning with Large Language ModelsCode1
ByteSized32: A Corpus and Challenge Task for Generating Task-Specific World Models Expressed as Text GamesCode1
Comprehensive Visual Question Answering on Point Clouds through Compositional Scene ManipulationCode1
A Semantic Space is Worth 256 Language Descriptions: Make Stronger Segmentation Models with Descriptive PropertiesCode1
How Can We Know When Language Models Know? On the Calibration of Language Models for Question AnsweringCode1
Exploring the Benefits of Training Expert Language Models over Instruction TuningCode1
Evidence-Aware Inferential Text Generation with Vector Quantised Variational AutoEncoderCode1
Does It Make Sense? And Why? A Pilot Study for Sense Making and ExplanationCode1
KELM: Knowledge Enhanced Pre-Trained Language Representations with Message Passing on Hierarchical Relational GraphsCode1
EventPlus: A Temporal Event Understanding PipelineCode1
Exploring AIGC Video Quality: A Focus on Visual Harmony, Video-Text Consistency and Domain Distribution GapCode1
Evaluating and Analyzing Relationship Hallucinations in Large Vision-Language ModelsCode1
Common Sense Beyond English: Evaluating and Improving Multilingual Language Models for Commonsense ReasoningCode1
Boosting Theory-of-Mind Performance in Large Language Models via PromptingCode1
Blow the Dog Whistle: A Chinese Dataset for Cant Understanding with Common Sense and World KnowledgeCode1
Common Sense Enhanced Knowledge-based Recommendation with Large Language ModelCode1
Do Multilingual Language Models Think Better in English?Code1
Learning Long-term Visual Dynamics with Region Proposal Interaction NetworksCode1
Learning to Detect Mobile Objects from LiDAR Scans Without LabelsCode1
LLM-Coordination: Evaluating and Analyzing Multi-agent Coordination Abilities in Large Language ModelsCode1
DialogSum: A Real-Life Scenario Dialogue Summarization DatasetCode1
Mitigating the Alignment Tax of RLHFCode1
DomainRAG: A Chinese Benchmark for Evaluating Domain-specific Retrieval-Augmented GenerationCode1
Event2Mind for Russian: Understanding Emotions and Intents in Texts. Corpus and Model for EvaluationCode1
Generative Data Augmentation for Commonsense ReasoningCode1
Improving Visual Commonsense in Language Models via Multiple Image GenerationCode1
Playing Codenames with Language Graphs and Word EmbeddingsCode1
An Improved Neural Baseline for Temporal Relation Extraction—0
Benchmarks for Automated Commonsense Reasoning: A Survey—0
Belief Scene Graphs: Expanding Partial Scenes with Objects through Computation of Expectation—0
An Hymn of an even Deeper Sentiment Analysis—0
LLM-Advisor: An LLM Benchmark for Cost-efficient Path Planning across Multiple Terrains—0
Behind the Scenes of an Evolving Event Cloze Test—0
Deep Haptic Model Predictive Control for Robot-Assisted Dressing—0
A Hierarchical Bayesian Model for Unsupervised Induction of Script Knowledge—0
Deep Distilling: automated code generation using explainable deep learning—0
Deep Reinforcement Learning-Based Approach for a Single Vehicle Persistent Surveillance Problem with Fuel Constraints—0
Deep Style Match for Complementary Recommendation—0
Back to Square One: Artifact Detection, Training and Commonsense Disentanglement in the Winograd Schema—0
Baby Intuitions Benchmark (BIB): Discerning the goals, preferences, and actions of others—0
An End-to-End Multi-task Learning Model for Fact Checking—0
A vision-grounded dataset for predicting typical locations for verbs—0
An Application of Pseudo-Log-Likelihoods to Natural Language Scoring—0
Abductive Reasoning as Self-Supervision for Common Sense Question Answering—0
A Vision for Semantically Enriched Data Science—0
A very preliminary analysis of DALL-E 2—0
An Aposteriorical Clusterability Criterion for k-Means++ and Simplicity of Clustering—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