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

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Showing 501–550 of 939 papers

TitleStatusHype
SERVAL: Synergy Learning between Vertical Models and LLMs towards Oracle-Level Zero-shot Medical Prediction—0
ShotgunWSD: An unsupervised algorithm for global word sense disambiguation inspired by DNA sequencing—0
Shrinkage Initialization for Smooth Learning of Neural Networks—0
SocialNLP 2018 EmotionX Challenge Overview: Recognizing Emotions in Dialogues—0
Soft Label PU Learning—0
SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving—0
Some Extensions of Probabilistic Logic—0
Some Preliminary Steps Towards Metaverse Logic—0
Sort Story: Sorting Jumbled Images and Captions into Stories—0
Spatial Knowledge Graph-Guided Multimodal Synthesis—0
SSN-NLP at SemEval-2020 Task 4: Text Classification and Generation on Common Sense Context Using Neural Networks—0
Stacking with Auxiliary Features for Visual Question Answering—0
Stating the Obvious: Extracting Visual Common Sense Knowledge—0
Stay on topic with Classifier-Free Guidance—0
Story Comprehension for Predicting What Happens Next—0
Story Generation with Commonsense Knowledge Graphs and Axioms—0
Strongly-Typed Agents are Guaranteed to Interact Safely—0
Structured Event Reasoning with Large Language Models—0
Summarize the Past to Predict the Future: Natural Language Descriptions of Context Boost Multimodal Object Interaction Anticipation—0
Sunny and Dark Outside?! Improving Answer Consistency in VQA through Entailed Question Generation—0
SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference—0
Sweetening Ontologies cont'd—0
Symbol Grounding via Chaining of Morphisms—0
Synthesizing Programmatic Reinforcement Learning Policies with Large Language Model Guided Search—0
Systematic Error Analysis of the Stanford Question Answering Dataset—0
Tabular Data Imputation: Choose KNN over Deep Learning—0
Tag Map: A Text-Based Map for Spatial Reasoning and Navigation with Large Language Models—0
TakeLab at SemEval-2017 Task 6: \#RankingHumorIn4Pages—0
TakeLab at SemEval-2018 Task12: Argument Reasoning Comprehension with Skip-Thought Vectors—0
TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs—0
TeamJUST at SemEval-2020 Task 4: Commonsense Validation and Explanation Using Ensembling Techniques—0
Telecom Language Models: Must They Be Large?—0
Tell Codec What Worth Compressing: Semantically Disentangled Image Coding for Machine with LMMs—0
Tell Me Why: Incentivizing Explanations—0
Temporal Common Sense Acquisition with Minimal Supervision—0
TETRIS: Towards Exploring the Robustness of Interactive Segmentation—0
TGEA: An Error-Annotated Dataset and Benchmark Tasks for TextGeneration from Pretrained Language Models—0
The Case for a Mixed-Initiative Collaborative Neuroevolution Approach—0
The Claude 3 Model Family: Opus, Sonnet, Haiku—0
The Collision of Quality and Technology with Reality—0
The Computational Principles of Learning Ability—0
The Embeddings World and Artificial General Intelligence—0
The ILASP system for Inductive Learning of Answer Set Programs—0
The Lottery LLM Hypothesis, Rethinking What Abilities Should LLM Compression Preserve?—0
The Neural Metric Factorization for Computational Drug Repositioning—0
The Physics of Text: Ontological Realism in Information Extraction—0
The Power of Question Translation Training in Multilingual Reasoning: Broadened Scope and Deepened Insights—0
The Quest for Visual Understanding: A Journey Through the Evolution of Visual Question Answering—0
The RatioLog Project: Rational Extensions of Logical Reasoning—0
The Rosetta Paradox: Domain-Specific Performance Inversions in Large Language Models—0
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All datasetsWinoGrandearc_challengearc_easyReCoRDCommonsenseQAPARusRuCoSRWSDBIG-bench (Causal Judgment)BIG-bench (Date Understanding)BIG-bench (Disambiguation QA)BIG-bench (Sports Understanding)

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