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 551–600 of 939 papers

TitleStatusHype
Toward a Better Understanding of Causality between Verbal Events: Extraction and Analysis of the Causal Power of Verb-Verb Associations—0
Toward a New Science of Common Sense—0
Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUE—0
Towards A Litmus Test for Common Sense—0
Towards Automated Error Analysis: Learning to Characterize Errors—0
Towards common-sense reasoning via conditional simulation: legacies of Turing in Artificial Intelligence—0
Towards Distributed MCMC Inference in Probabilistic Knowledge Bases—0
Towards Generalizable Neuro-Symbolic Systems for Commonsense Question Answering—0
Towards Learning Geometric Eigen-Lengths Crucial for Fitting Tasks—0
Towards Learning Object Affordance Priors from Technical Texts—0
Towards Robot-Centric Conceptual Knowledge Acquisition—0
Towards Semantic-based Hybrid Machine Translation between Bulgarian and English—0
Towards the Detection of a Semantic Gap in the Chain of Commonsense Knowledge Triples—0
Towards Top-Down Reasoning: An Explainable Multi-Agent Approach for Visual Question Answering—0
TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors—0
Transforming the Data Transcription and Analysis Tool Metadata and Labels into a Linguistic Linked Open Data Cloud Resource—0
Trans-KBLSTM: An External Knowledge Enhanced Transformer BiLSTM Model for Tabular Reasoning—0
Translating SUMO-K to Higher-Order Set Theory—0
Transliteration and alignment of parallel texts from Cyrillic to Latin—0
TR at SemEval-2020 Task 4: Exploring the Limits of Language-model-based Common Sense Validation—0
Trimming a consistent OWL knowledge base, relying on linguistic evidence—0
True or False: Does the Deep Learning Model Learn to Detect Rumors?—0
TTCS^: a Vectorial Resource for Computing Conceptual Similarity—0
UAV-VLN: End-to-End Vision Language guided Navigation for UAVs—0
UKnow: A Unified Knowledge Protocol with Multimodal Knowledge Graph Datasets for Reasoning and Vision-Language Pre-Training—0
Unbiasing through Textual Descriptions: Mitigating Representation Bias in Video Benchmarks—0
Understanding In-Context Learning with a Pelican Soup Framework—0
Understanding Multimodal Procedural Knowledge by Sequencing Multimodal Instructional Manuals—0
Understanding Multimodal Procedural Knowledge by Sequencing Multimodal Instructional Manuals—0
Understanding Satirical Articles Using Common-Sense—0
Understanding Spatial Relations through Multiple Modalities—0
Unreasonable Effectiveness of Rule-Based Heuristics in Solving Russian SuperGLUE Tasks—0
Unsupervised Common Sense Relation Extraction—0
Unsupervised Deep Structured Semantic Models for Commonsense Reasoning—0
Unsupervised Induction of Contingent Event Pairs from Film Scenes—0
UoR at SemEval-2020 Task 4: Pre-trained Sentence Transformer Models for Commonsense Validation and Explanation—0
Using ConceptNet to Teach Common Sense to an Automated Theorem Prover—0
Using Conceptual Class Attributes to Characterize Social Media Users—0
Using English Dictionaries to generate Commonsense Knowledge in Natural Language—0
Using GPT-4 to guide causal machine learning—0
Using Large Language Models for the Interpretation of Building Regulations—0
Using Web Co-occurrence Statistics for Improving Image Categorization—0
V-Coder: Adaptive AutoEncoder for Semantic Disclosure in Knowledge Graphs—0
ViRAC: A Vision-Reasoning Agent Head Movement Control Framework in Arbitrary Virtual Environments—0
Vision-Based Generic Potential Function for Policy Alignment in Multi-Agent Reinforcement Learning—0
Vision Language Pre-training by Contrastive Learning with Cross-Modal Similarity Regulation—0
Vision-Language Models Provide Promptable Representations for Reinforcement Learning—0
VisualBackProp for learning using privileged information with CNNs—0
Visual-O1: Understanding Ambiguous Instructions via Multi-modal Multi-turn Chain-of-thoughts Reasoning—0
Visual Question Answering as Reading Comprehension—0
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All datasetsWinoGrandearc_challengearc_easyReCoRDCommonsenseQAPARusRuCoSRWSDBIG-bench (Causal Judgment)BIG-bench (Date Understanding)BIG-bench (Disambiguation QA)BIG-bench (Sports Understanding)

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