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 351400 of 939 papers

TitleStatusHype
Investigating the Application of Common-Sense Knowledge-Base for Identifying Term Obfuscation in Adversarial Communication0
FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering0
Features of Verb Complements in Co-composition: A case study of Chinese baking verb using Weibo corpus0
Generating Out-Of-Distribution Scenarios Using Language Models0
Combining PCFG-LA Models with Dual Decomposition: A Case Study with Function Labels and Binarization0
Combining Fast and Slow Thinking for Human-like and Efficient Navigation in Constrained Environments0
Extraction of Common-Sense Relations from Procedural Task Instructions using BERT0
Geo-distinctive Visual Element Matching for Location Estimation of Images0
Extracting Zero-shot Common Sense from Large Language Models for Robot 3D Scene Understanding0
Collaborative Filtering for Predicting User Preferences for Organizing Objects0
Comparing Apples to Oranges: A Dataset & Analysis of LLM Humour Understanding from Traditional Puns to Topical Jokes0
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts0
A Service-Oriented Architecture for Assisting the Authoring of Semantic Crowd Maps0
Interactive Learning of Spatial Knowledge for Text to 3D Scene Generation0
GLRD: Global-Local Collaborative Reason and Debate with PSL for 3D Open-Vocabulary Detection0
GO FIGURE: A Meta Evaluation of Factuality in Summarization0
Good Automatic Authentication Question Generation0
Interpretable Visual Question Answering via Reasoning Supervision0
iPerceive: Applying Common-Sense Reasoning to Multi-Modal Dense Video Captioning and Video Question Answering0
GPT-4V Takes the Wheel: Promises and Challenges for Pedestrian Behavior Prediction0
Computational Argumentation: A Journey Beyond Semantics, Logic, Opinions, and Easy Tasks0
GrapeQA: GRaph Augmentation and Pruning to Enhance Question-Answering0
Computational principles of intelligence: learning and reasoning with neural networks0
Is “My Favorite New Movie” My Favorite Movie? Probing the Understanding of Recursive Noun Phrases0
KARGEN: Knowledge-enhanced Automated Radiology Report Generation Using Large Language Models0
Grounding Language Plans in Demonstrations Through Counterfactual Perturbations0
Extended HowNet 2.0 -- An Entity-Relation Common-Sense Representation Model0
Cluster Flow: how a hierarchical clustering layer make allows deep-NNs more resilient to hacking, more human-like and easily implements relational reasoning0
Hallucination Detection in Foundation Models for Decision-Making: A Flexible Definition and Review of the State of the Art0
Handling Multiword Expressions in Causality Estimation0
Exploring Unsupervised Pretraining and Sentence Structure Modelling for Winograd Schema Challenge0
Integrating a Heterogeneous Graph with Entity-aware Self-attention using Relative Position Labels for Reading Comprehension Model0
A Large Scale Database of Strongly-related Events in Japanese0
Can We Trust Embodied Agents? Exploring Backdoor Attacks against Embodied LLM-based Decision-Making Systems0
HGSGNLP at IEST 2018: An Ensemble of Machine Learning and Deep Neural Architectures for Implicit Emotion Classification in Tweets0
A Rule-Based Approach to Aspect Extraction from Product Reviews0
Hierarchical Relational Inference0
Consolidating Commonsense Knowledge0
Constrained Text Generation with Global Guidance -- Case Study on CommonGen0
InstructionBench: An Instructional Video Understanding Benchmark0
Integration of knowledge and data in machine learning0
How Factuality Determines Sentiment Inferences0
How Pre-trained Word Representations Capture Commonsense Physical Comparisons0
"Tidy Up the Table": Grounding Common-sense Objective for Tabletop Object Rearrangement0
How to Understand Named Entities: Using Common Sense for News Captioning0
HR@JUST Team at SemEval-2020 Task 4: The Impact of RoBERTa Transformer for Evaluation Common Sense Understanding0
Context-based Natural Language Processing for GIS-based Vague Region Visualization0
Human-Object Interaction from Human-Level Instructions0
Explore before Moving: A Feasible Path Estimation and Memory Recalling Framework for Embodied Navigation0
Exploiting Proximity-Aware Tasks for Embodied Social Navigation0
Show:102550
← PrevPage 8 of 19Next →

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