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
Decentralized Vehicle Coordination: The Berkeley DeepDrive Drone Dataset and Consensus-Based Models0
Multimodal Analysis Of Google Bard And GPT-Vision: Experiments In Visual Reasoning0
Deep Distilling: automated code generation using explainable deep learning0
Deep Haptic Model Predictive Control for Robot-Assisted Dressing0
Deep Reinforcement Learning-Based Approach for a Single Vehicle Persistent Surveillance Problem with Fuel Constraints0
Deep Style Match for Complementary Recommendation0
Deep Unsupervised Hashing with Latent Semantic Components0
DEEPYANG at SemEval-2020 Task 4: Using the Hidden Layer State of BERT Model for Differentiating Common Sense0
DELTA: Decomposed Efficient Long-Term Robot Task Planning using Large Language Models0
Design and Implementation of Linked Planning Domain Definition Language0
Design Knowledge Representation with Technology Semantic Network0
Despite "super-human" performance, current LLMs are unsuited for decisions about ethics and safety0
Detect2Interact: Localizing Object Key Field in Visual Question Answering (VQA) with LLMs0
Detecting COVID-19 Conspiracy Theories with Transformers and TF-IDF0
Developing a concept-level knowledge base for sentiment analysis in Singlish0
Bootstrapping Developmental AIs: From Simple Competences to Intelligent Human-Compatible AIs0
DialogSum Challenge: Summarizing Real-Life Scenario Dialogues0
Diffusion as Reasoning: Enhancing Object Goal Navigation with LLM-Biased Diffusion Model0
DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery0
Distributional semantics for ontology verification0
DOCK: Detecting Objects by transferring Common-sense Knowledge0
Does Knowledge Help General NLU? An Empirical Study0
Do ever larger octopi still amplify reporting biases? Evidence from judgments of typical colour0
Do I look like a `cat.n.01` to you? A Taxonomy Image Generation Benchmark0
Do Language Embeddings Capture Scales?0
Do Language Models Have Common Sense?0
Don't Just Listen, Use Your Imagination: Leveraging Visual Common Sense for Non-Visual Tasks0
dPASP: A Comprehensive Differentiable Probabilistic Answer Set Programming Environment For Neurosymbolic Learning and Reasoning0
Dutch Humor Detection by Generating Negative Examples0
Dynamic Integration of Background Knowledge in Neural NLU Systems0
Ecological Semantics: Programming Environments for Situated Language Understanding0
Russian SuperGLUE 1.1: Revising the Lessons not Learned by Russian NLP models0
SAGE: Smart home Agent with Grounded Execution0
SAGE: Structured Attribute Value Generation for Billion-Scale Product Catalogs0
Scalar Adjective Identification and Multilingual Ranking0
ScanEdit: Hierarchically-Guided Functional 3D Scan Editing0
Scenethesis: A Language and Vision Agentic Framework for 3D Scene Generation0
Scientia Potentia Est -- On the Role of Knowledge in Computational Argumentation0
Seeing is Knowing! Fact-based Visual Question Answering using Knowledge Graph Embeddings0
Seeing the Unseen: Visual Common Sense for Semantic Placement0
Seeing the World through Text: Evaluating Image Descriptions for Commonsense Reasoning in Machine Reading Comprehension0
Segmentation Guided Attention Networks for Visual Question Answering0
Semantic Parsing for Text to 3D Scene Generation0
Semantic Segmentation of RGBD Images With Mutex Constraints0
Semantic Vector Spaces for Broadening Consideration of Consequences0
SemEval-2018 Task 12: The Argument Reasoning Comprehension Task0
SemEval-2024 Task 9: BRAINTEASER: A Novel Task Defying Common Sense0
Sense Perception Common Sense Relationships0
Sentiment Analysis Using a Novel Human Computation Game0
Sentiment Analysis - What are we talking about?0
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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