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 351375 of 939 papers

TitleStatusHype
FirePlace: Geometric Refinements of LLM Common Sense Reasoning for 3D Object Placement0
Generating Interactive Worlds with Text0
Ask Me What You Need: Product Retrieval using Knowledge from GPT-30
Generating Out-Of-Distribution Scenarios Using Language Models0
COMMA-DEER: COmmon-sense Aware Multimodal Multitask Approach for Detection of Emotion and Emotional Reasoning in Conversations0
ADEPT: An Adjective-Dependent Plausibility Task0
Fine-grained evaluation of Quality Estimation for Machine translation based on a linguistically motivated Test Suite0
Geo-distinctive Visual Element Matching for Location Estimation of Images0
IIT (BHU): System Description for LSDSem'17 Shared Task0
FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering0
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
Features of Verb Complements in Co-composition: A case study of Chinese baking verb using Weibo corpus0
Combining PCFG-LA Models with Dual Decomposition: A Case Study with Function Labels and Binarization0
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
Combining Fast and Slow Thinking for Human-like and Efficient Navigation in Constrained Environments0
HybridVLA: Collaborative Diffusion and Autoregression in a Unified Vision-Language-Action Model0
GPT-4V Takes the Wheel: Promises and Challenges for Pedestrian Behavior Prediction0
Extraction of Common-Sense Relations from Procedural Task Instructions using BERT0
GrapeQA: GRaph Augmentation and Pruning to Enhance Question-Answering0
Computational principles of intelligence: learning and reasoning with neural networks0
Extracting Zero-shot Common Sense from Large Language Models for Robot 3D Scene Understanding0
Collaborative Filtering for Predicting User Preferences for Organizing Objects0
Show:102550
← PrevPage 15 of 38Next →

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