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 701–750 of 939 papers

TitleStatusHype
Towards Generalizable Neuro-Symbolic Systems for Commonsense Question Answering—0
QASC: A Dataset for Question Answering via Sentence CompositionCode0
Assisting human experts in the interpretation of their visual process: A case study on assessing copper surface adhesive potency—0
Learning Continuous 3D Reconstructions for Geometrically Aware Grasping—0
Linguistic Embeddings as a Common-Sense Knowledge Repository: Challenges and Opportunities—0
Why Does the VQA Model Answer No?: Improving Reasoning through Visual and Linguistic Inference—0
Measuring Numerical Common Sense: Is A Word Embedding Approach Effective?—0
Conversational AI : Open Domain Question Answering and Commonsense Reasoning—0
Bridging Visual Perception with Contextual Semantics for Understanding Robot Manipulation Tasks—0
Probabilistic framework for solving Visual Dialog—0
Sunny and Dark Outside?! Improving Answer Consistency in VQA through Entailed Question Generation—0
Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question AnsweringCode0
Abductive Reasoning as Self-Supervision for Common Sense Question Answering—0
An Improved Neural Baseline for Temporal Relation Extraction—0
Visual Question Answering using Deep Learning: A Survey and Performance AnalysisCode0
Improving Neural Story Generation by Targeted Common Sense GroundingCode0
DAST Model: Deciding About Semantic Complexity of a Text—0
Align, Mask and Select: A Simple Method for Incorporating Commonsense Knowledge into Language Representation Models—0
Reasoning-Driven Question-Answering for Natural Language Understanding—0
Learn How to Cook a New Recipe in a New House: Using Map Familiarization, Curriculum Learning, and Bandit Feedback to Learn Families of Text-Based Adventure GamesCode0
Knowledge Aware Semantic Concept Expansion for Image-Text Matching—0
Processamento de linguagem natural em Português e aprendizagem profunda para o domínio de Óleo e Gás—0
A Hybrid Neural Network Model for Commonsense Reasoning—0
Learning Emphasis Selection for Written Text in Visual Media from Crowd-Sourced Label DistributionsCode0
Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation ExtractionCode0
A Statistical View on Synthetic Aperture Imaging for Occlusion Removal—0
Explain Yourself! Leveraging Language Models for Commonsense ReasoningCode0
CODAH: An Adversarially-Authored Question Answering Dataset for Common SenseCode0
Learning Open Information Extraction of Implicit Relations from Reading Comprehension Datasets—0
Do Language Models Have Common Sense?—0
Enabling Robots to Understand Incomplete Natural Language Instructions Using Commonsense Reasoning—0
SocialIQA: Commonsense Reasoning about Social InteractionsCode0
Exploring Unsupervised Pretraining and Sentence Structure Modelling for Winograd Schema Challenge—0
CITE: A Corpus of Image-Text Discourse RelationsCode0
Interaction-aware Decision Making with Adaptive Strategies under Merging Scenarios—0
CODAH: An Adversarially Authored Question-Answer Dataset for Common SenseCode0
Unsupervised Deep Structured Semantic Models for Commonsense Reasoning—0
Asking the Right Question: Inferring Advice-Seeking Intentions from Personal NarrativesCode0
Ranking and Selecting Multi-Hop Knowledge Paths to Better Predict Human NeedsCode0
RoboCSE: Robot Common Sense Embedding—0
Visual search and recognition for robot task execution and monitoring—0
Learning Spatial Common Sense with Geometry-Aware Recurrent Networks—0
We Usually Don't Like Going to the Dentist: Using Common Sense to Detect Irony on Twitter—0
Visual Question Answering as Reading Comprehension—0
Attentioned Convolutional LSTM InpaintingNetwork for Anomaly Detection in Videos—0
Sense Perception Common Sense Relationships—0
Compositional Language Understanding with Text-based Relational ReasoningCode0
How Reasonable are Common-Sense Reasoning Tasks: A Case-Study on the Winograd Schema Challenge and SWAGCode0
The Knowref Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora ResolutionCode0
An End-to-End Multi-task Learning Model for Fact Checking—0
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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