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 201225 of 939 papers

TitleStatusHype
FLIP Reasoning ChallengeCode0
Shrinkage Initialization for Smooth Learning of Neural Networks0
What the HellaSwag? On the Validity of Common-Sense Reasoning BenchmarksCode0
JEPA4Rec: Learning Effective Language Representations for Sequential Recommendation via Joint Embedding Predictive Architecture0
InstructionBench: An Instructional Video Understanding Benchmark0
Proposition of Affordance-Driven Environment Recognition Framework Using Symbol Networks in Large Language Models0
DynMoLE: Boosting Mixture of LoRA Experts Fine-Tuning with a Hybrid Routing MechanismCode0
WinoWhat: A Parallel Corpus of Paraphrased WinoGrande Sentences with Common Sense Categorization0
Information Gain Is Not All You NeedCode0
GLRD: Global-Local Collaborative Reason and Debate with PSL for 3D Open-Vocabulary Detection0
Unbiasing through Textual Descriptions: Mitigating Representation Bias in Video Benchmarks0
A Study on Neuro-Symbolic Artificial Intelligence: Healthcare Perspectives0
Improving Preference Extraction In LLMs By Identifying Latent Knowledge Through Classifying Probes0
Don't Fight Hallucinations, Use Them: Estimating Image Realism using NLI over Atomic FactsCode0
Do I look like a `cat.n.01` to you? A Taxonomy Image Generation Benchmark0
HybridVLA: Collaborative Diffusion and Autoregression in a Unified Vision-Language-Action Model0
LVLM-Compress-Bench: Benchmarking the Broader Impact of Large Vision-Language Model CompressionCode0
The Box is in the Pen: Evaluating Commonsense Reasoning in Neural Machine TranslationCode0
LLM-Advisor: An LLM Benchmark for Cost-efficient Path Planning across Multiple Terrains0
Code-as-Symbolic-Planner: Foundation Model-Based Robot Planning via Symbolic Code Generation0
Personalized Causal Graph Reasoning for LLMs: A Case Study on Dietary Recommendations0
FRIDA to the Rescue! Analyzing Synthetic Data Effectiveness in Object-Based Common Sense Reasoning for Disaster Response0
The Lottery LLM Hypothesis, Rethinking What Abilities Should LLM Compression Preserve?0
KnowZRel: Common Sense Knowledge-based Zero-Shot Relationship Retrieval for Generalised Scene Graph GenerationCode0
PredictaBoard: Benchmarking LLM Score PredictabilityCode0
Show:102550
← PrevPage 9 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