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 101150 of 939 papers

TitleStatusHype
Expressive Scene Graph Generation Using Commonsense Knowledge Infusion for Visual Understanding and ReasoningCode1
Do Multilingual Language Models Think Better in English?Code1
Memory Sharing for Large Language Model based AgentsCode1
Finding Effective Security Strategies through Reinforcement Learning and Self-PlayCode1
Mention Flags (MF): Constraining Transformer-based Text GeneratorsCode1
Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the WildCode1
EventPlus: A Temporal Event Understanding PipelineCode1
Does It Make Sense? And Why? A Pilot Study for Sense Making and ExplanationCode1
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attentionCode1
Generative Data Augmentation for Commonsense ReasoningCode1
A Semantic Space is Worth 256 Language Descriptions: Make Stronger Segmentation Models with Descriptive PropertiesCode1
Learning to Detect Mobile Objects from LiDAR Scans Without LabelsCode1
Leveraging Large (Visual) Language Models for Robot 3D Scene UnderstandingCode1
Learning Long-term Visual Dynamics with Region Proposal Interaction NetworksCode1
CheXWorld: Exploring Image World Modeling for Radiograph Representation LearningCode1
Large Language Models are Better Reasoners with Self-VerificationCode1
Grounding Consistency: Distilling Spatial Common Sense for Precise Visual Relationship DetectionCode1
Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot LearnersCode1
Large Language Models as Evaluators for Recommendation ExplanationsCode1
Are Large Language Models Temporally Grounded?Code1
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question AnsweringCode1
AR-Diffusion: Auto-Regressive Diffusion Model for Text GenerationCode1
A Hitchhikers Guide to Fine-Grained Face Forgery Detection Using Common Sense ReasoningCode1
How Can We Know When Language Models Know? On the Calibration of Language Models for Question AnsweringCode1
3D Visual Illusion Depth EstimationCode1
Layout-aware Dreamer for Embodied Referring Expression GroundingCode1
Language Models are Unsupervised Multitask LearnersCode1
Conversational Word Embedding for Retrieval-Based Dialog SystemCode1
Large Language Models Are Neurosymbolic ReasonersCode1
KELM: Knowledge Enhanced Pre-Trained Language Representations with Message Passing on Hierarchical Relational GraphsCode1
CommonsenseQA: A Question Answering Challenge Targeting Commonsense KnowledgeCode1
ConceptBert: Concept-Aware Representation for Visual Question AnsweringCode1
KETM:A Knowledge-Enhanced Text Matching methodCode1
Large Language Models Are Not Strong Abstract ReasonersCode1
Common Sense Beyond English: Evaluating and Improving Multilingual Language Models for Commonsense ReasoningCode1
ImageNetVC: Zero- and Few-Shot Visual Commonsense Evaluation on 1000 ImageNet CategoriesCode1
ByteSized32: A Corpus and Challenge Task for Generating Task-Specific World Models Expressed as Text GamesCode1
Evaluation Toolkit For Robustness Testing Of Automatic Essay Scoring SystemsCode1
Improving Visual Commonsense in Language Models via Multiple Image GenerationCode1
Boosting Theory-of-Mind Performance in Large Language Models via PromptingCode1
Language Agents Meet Causality -- Bridging LLMs and Causal World ModelsCode1
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
A Dataset for Interactive Vision-Language Navigation with Unknown Command FeasibilityCode1
CBVS: A Large-Scale Chinese Image-Text Benchmark for Real-World Short Video Search ScenariosCode1
Chain of Images for Intuitively ReasoningCode1
Comprehensive Visual Question Answering on Point Clouds through Compositional Scene ManipulationCode1
DialogSum: A Real-Life Scenario Dialogue Summarization DatasetCode1
IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language ModelsCode1
Mitigating the Alignment Tax of RLHFCode1
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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