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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 701–750 of 939 papers

TitleStatusHype
Automatic Identification of Age-Appropriate Ratings of Song Lyrics—0
Automatic semantic relation extraction from Portuguese texts—0
Automatic Text Generation by Learning from Literary Structures—0
A very preliminary analysis of DALL-E 2—0
A Vision for Semantically Enriched Data Science—0
A vision-grounded dataset for predicting typical locations for verbs—0
Baby Intuitions Benchmark (BIB): Discerning the goals, preferences, and actions of others—0
Back to Square One: Artifact Detection, Training and Commonsense Disentanglement in the Winograd Schema—0
Behind the Scenes of an Evolving Event Cloze Test—0
Belief Scene Graphs: Expanding Partial Scenes with Objects through Computation of Expectation—0
Benchmarks for Automated Commonsense Reasoning: A Survey—0
Piecing Together Clues: A Benchmark for Evaluating the Detective Skills of Large Language Models—0
BLCU\_NLP at SemEval-2018 Task 12: An Ensemble Model for Argument Reasoning Based on Hierarchical Attention—0
BLCU-NLP at SemEval-2020 Task 5: Data Augmentation for Efficient Counterfactual Detecting—0
BloombergGPT: A Large Language Model for Finance—0
Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM—0
Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images—0
Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models—0
Building Human-like Communicative Intelligence: A Grounded Perspective—0
Can Models Learn Skill Composition from Examples?—0
Can Transformers Reason About Effects of Actions?—0
CaseEdit: Enhancing Localized Commonsense Reasoning via Null-Space Constrained Knowledge Editing in Small Parameter Language Models—0
Categorical Traffic Transformer: Interpretable and Diverse Behavior Prediction with Tokenized Latent—0
CauseJudger: Identifying the Cause with LLMs for Abductive Logical Reasoning—0
CheckManual: A New Challenge and Benchmark for Manual-based Appliance Manipulation—0
CHORUS: Learning Canonicalized 3D Human-Object Spatial Relations from Unbounded Synthesized Images—0
CHORUS : Learning Canonicalized 3D Human-Object Spatial Relations from Unbounded Synthesized Images—0
CLaSP: Learning Concepts for Time-Series Signals from Natural Language Supervision—0
Cluster Flow: how a hierarchical clustering layer make allows deep-NNs more resilient to hacking, more human-like and easily implements relational reasoning—0
Collaborative Filtering for Predicting User Preferences for Organizing Objects—0
Combining Fast and Slow Thinking for Human-like and Efficient Navigation in Constrained Environments—0
Combining PCFG-LA Models with Dual Decomposition: A Case Study with Function Labels and Binarization—0
COMMA-DEER: COmmon-sense Aware Multimodal Multitask Approach for Detection of Emotion and Emotional Reasoning in Conversations—0
Commonsense about Human Senses: Labeled Data Collection Processes—0
Common-Sense Bias Modeling for Classification Tasks—0
Common Sense Is All You Need—0
Commonsense Knowledge from Scene Graphs for Textual Environments—0
Commonsense Knowledge in Wikidata—0
Common Sense Knowledge Learning for Open Vocabulary Neural Reasoning: A First View into Chronic Disease Literature—0
Common Sense Knowledge, Ontology and Text Mining for Implicit Requirements—0
Commonsense Ontology Micropatterns—0
CommonsenseQA 2.0: Exposing the Limits of AI through Gamification—0
Comparing Apples to Oranges: A Dataset & Analysis of LLM Humour Understanding from Traditional Puns to Topical Jokes—0
Comprehension Based Question Answering using Bloom's Taxonomy—0
Comprehension Based Question Answering using Bloom’s Taxonomy—0
Comprehensive Annotation of Various Types of Temporal Information on the Time Axis—0
Computational Argumentation: A Journey Beyond Semantics, Logic, Opinions, and Easy Tasks—0
Computational principles of intelligence: learning and reasoning with neural networks—0
Computing Sentiment Scores of Verb Phrases for Vietnamese—0
Co-NavGPT: Multi-Robot Cooperative Visual Semantic Navigation Using Vision Language Models—0
Show:102550
← PrevPage 15 of 19Next →
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