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 151175 of 939 papers

TitleStatusHype
Evaluating and Analyzing Relationship Hallucinations in Large Vision-Language ModelsCode1
CommonsenseQA: A Question Answering Challenge Targeting Commonsense KnowledgeCode1
LLM-Coordination: Evaluating and Analyzing Multi-agent Coordination Abilities in Large Language ModelsCode1
EventPlus: A Temporal Event Understanding PipelineCode1
Finding Effective Security Strategies through Reinforcement Learning and Self-PlayCode1
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
HeadlineCause: A Dataset of News Headlines for Detecting CausalitiesCode1
DomainRAG: A Chinese Benchmark for Evaluating Domain-specific Retrieval-Augmented GenerationCode1
Do Multilingual Language Models Think Better in English?Code1
Does It Make Sense? And Why? A Pilot Study for Sense Making and ExplanationCode1
ImageNetVC: Zero- and Few-Shot Visual Commonsense Evaluation on 1000 ImageNet CategoriesCode1
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
DialogSum: A Real-Life Scenario Dialogue Summarization DatasetCode1
Common Sense Beyond English: Evaluating and Improving Multilingual Language Models for Commonsense ReasoningCode1
Common Sense or World Knowledge? Investigating Adapter-Based Knowledge Injection into Pretrained TransformersCode1
Mitigating the Alignment Tax of RLHFCode1
Common Sense Enhanced Knowledge-based Recommendation with Large Language ModelCode1
Large Language Models Are Neurosymbolic ReasonersCode1
Large Language Models are Better Reasoners with Self-VerificationCode1
IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language ModelsCode1
Layout-aware Dreamer for Embodied Referring Expression GroundingCode1
Learning Long-term Visual Dynamics with Region Proposal Interaction NetworksCode1
MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem SolvingCode1
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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