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 101–125 of 939 papers

TitleStatusHype
How Can We Know When Language Models Know? On the Calibration of Language Models for Question AnsweringCode1
Do Multilingual Language Models Think Better in English?Code1
Common Sense or World Knowledge? Investigating Adapter-Based Knowledge Injection into Pretrained TransformersCode1
HAZARD Challenge: Embodied Decision Making in Dynamically Changing EnvironmentsCode1
Learning Long-term Visual Dynamics with Region Proposal Interaction NetworksCode1
Learning to Detect Mobile Objects from LiDAR Scans Without LabelsCode1
Does It Make Sense? And Why? A Pilot Study for Sense Making and ExplanationCode1
CommonsenseQA: A Question Answering Challenge Targeting Commonsense KnowledgeCode1
LLM-Coordination: Evaluating and Analyzing Multi-agent Coordination Abilities in Large Language ModelsCode1
Event2Mind for Russian: Understanding Emotions and Intents in Texts. Corpus and Model for EvaluationCode1
HeadlineCause: A Dataset of News Headlines for Detecting CausalitiesCode1
Human Parity on CommonsenseQA: Augmenting Self-Attention with External AttentionCode1
Exploring AIGC Video Quality: A Focus on Visual Harmony, Video-Text Consistency and Domain Distribution GapCode1
Exploring the Benefits of Training Expert Language Models over Instruction TuningCode1
A Semantic Space is Worth 256 Language Descriptions: Make Stronger Segmentation Models with Descriptive PropertiesCode1
Comprehensive Visual Question Answering on Point Clouds through Compositional Scene ManipulationCode1
Common Sense Beyond English: Evaluating and Improving Multilingual Language Models for Commonsense ReasoningCode1
Grounding Consistency: Distilling Spatial Common Sense for Precise Visual Relationship DetectionCode1
CheXWorld: Exploring Image World Modeling for Radiograph Representation LearningCode1
Are Large Language Models Temporally Grounded?Code1
Model Surgery: Modulating LLM's Behavior Via Simple Parameter EditingCode1
mT5: A massively multilingual pre-trained text-to-text transformerCode1
AR-Diffusion: Auto-Regressive Diffusion Model for Text GenerationCode1
Fusing Context Into Knowledge Graph for Commonsense Question AnsweringCode1
3D Visual Illusion Depth EstimationCode1
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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