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 451500 of 939 papers

TitleStatusHype
Vision Language Pre-training by Contrastive Learning with Cross-Modal Similarity Regulation0
Cluster Flow: how a hierarchical clustering layer make allows deep-NNs more resilient to hacking, more human-like and easily implements relational reasoning0
Reporting delays: a widely neglected impact factor in COVID-19 forecasts0
A Group-Specific Approach to NLP for Hate Speech DetectionCode0
BloombergGPT: A Large Language Model for Finance0
Humans in Humans Out: On GPT Converging Toward Common Sense in both Success and Failure0
TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs0
Converging Measures and an Emergent Model: A Meta-Analysis of Human-Automation Trust Questionnaires0
GrapeQA: GRaph Augmentation and Pruning to Enhance Question-Answering0
Mind meets machine: Unravelling GPT-4's cognitive psychology0
FVQA 2.0: Introducing Adversarial Samples into Fact-based Visual Question Answering0
Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images0
LUKE-Graph: A Transformer-based Approach with Gated Relational Graph Attention for Cloze-style Reading Comprehension0
A primer on getting neologisms from foreign languages to under-resourced languages0
Do Machine Learning Models Learn Statistical Rules Inferred from Data?Code0
A Vision for Semantically Enriched Data Science0
HL Dataset: Visually-grounded Description of Scenes, Actions and RationalesCode0
Framework for Certification of AI-Based Systems0
Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks0
UKnow: A Unified Knowledge Protocol with Multimodal Knowledge Graph Datasets for Reasoning and Vision-Language Pre-Training0
Benchmarks for Automated Commonsense Reasoning: A Survey0
Witscript 2: A System for Generating Improvised Jokes Without Wordplay0
Planning Automated Driving with Accident Experience Referencing and Common-sense Inferencing0
Mathematics, word problems, common sense, and artificial intelligence0
Summarize the Past to Predict the Future: Natural Language Descriptions of Context Boost Multimodal Object Interaction Anticipation0
Witscript 3: A Hybrid AI System for Improvising Jokes in a Conversation0
A Theory of Human-Like Few-Shot Learning0
CHORUS : Learning Canonicalized 3D Human-Object Spatial Relations from Unbounded Synthesized Images0
Despite "super-human" performance, current LLMs are unsuited for decisions about ethics and safety0
VASR: Visual Analogies of Situation RecognitionCode0
Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUE0
Legal Prompting: Teaching a Language Model to Think Like a Lawyer0
SimpleMind adds thinking to deep neural networksCode0
Exploiting Proximity-Aware Tasks for Embodied Social Navigation0
On Utilizing Relationships for Transferable Few-Shot Fine-Grained Object Detection0
DiffG-RL: Leveraging Difference between State and Common SenseCode0
A mathematical theory of super-resolution and two-point resolution0
A Unified Model for Video Understanding and Knowledge Embedding with Heterogeneous Knowledge Graph Dataset0
Is the Elephant Flying? Resolving Ambiguities in Text-to-Image Generative Models0
CAPE: Corrective Actions from Precondition Errors using Large Language Models0
Eliciting Knowledge from Large Pre-Trained Models for Unsupervised Knowledge-Grounded ConversationCode0
Knowledge-in-Context: Towards Knowledgeable Semi-Parametric Language Models0
GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational ComplexityCode0
LMPriors: Pre-Trained Language Models as Task-Specific Priors0
Large Language Models Can Self-Improve0
Commonsense Knowledge from Scene Graphs for Textual Environments0
Perplexity from PLM Is Unreliable for Evaluating Text Quality0
A survey of Identification and mitigation of Machine Learning algorithmic biases in Image Analysis0
Modular Approach to Machine Reading Comprehension: Mixture of Task-Aware Experts0
Robot Task Planning and Situation Handling in Open Worlds0
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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