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 851900 of 939 papers

TitleStatusHype
Computing Sentiment Scores of Verb Phrases for Vietnamese0
Good Automatic Authentication Question Generation0
Content selection as semantic-based ontology exploration0
A Discourse-Annotated Corpus of Conjoined VPs0
Event Embeddings for Semantic Script Modeling0
Improving Text-to-Pictograph Translation Through Word Sense Disambiguation0
How Factuality Determines Sentiment Inferences0
Most ``babies'' are ``little'' and most ``problems'' are ``huge'': Compositional Entailment in Adjective-Nouns0
Learning Prototypical Event Structure from Photo Albums0
Unsupervised Learning from Continuous Video in a Scalable Predictive Recurrent NetworkCode0
Sort Story: Sorting Jumbled Images and Captions into Stories0
FVQA: Fact-based Visual Question Answering0
Constructing a Dictionary Describing Feature Changes of Arguments in Event Sentences0
Regularizing Relation Representations by First-order Implications0
An Hymn of an even Deeper Sentiment Analysis0
The Physics of Text: Ontological Realism in Information Extraction0
Towards Semantic-based Hybrid Machine Translation between Bulgarian and English0
Automatic Text Generation by Learning from Literary Structures0
Sentiment Analysis - What are we talking about?0
Stating the Obvious: Extracting Visual Common Sense Knowledge0
Embedding Open-domain Common-sense Knowledge from Text0
Automatic Enrichment of WordNet with Common-Sense Knowledge0
Resolving Language and Vision Ambiguities Together: Joint Segmentation & Prepositional Attachment Resolution in Captioned Scenes0
Geo-distinctive Visual Element Matching for Location Estimation of Images0
Forecasting Social Navigation in Crowded Complex Scenes0
Potential and Limits of Using Post-edits as Reference Translations for MT Evaluation0
Understanding Satirical Articles Using Common-Sense0
Collaborative Filtering for Predicting User Preferences for Organizing Objects0
CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional DataCode0
Semantic Segmentation of RGBD Images With Mutex Constraints0
Learning Common Sense Through Visual Abstraction0
Visual Word2Vec (vis-w2v): Learning Visually Grounded Word Embeddings Using Abstract ScenesCode0
Reasoning in Vector Space: An Exploratory Study of Question Answering0
Modeling User Exposure in RecommendationCode0
The Computational Principles of Learning Ability0
Measuring an Artificial Intelligence System's Performance on a Verbal IQ Test For Young Children0
Learning the Impact and Behavior of Syntactic Structure: A Case Study in Semantic Textual Similarity0
Mise en Place: Unsupervised Interpretation of Instructional Recipes0
A Strong Lexical Matching Method for the Machine Comprehension Test0
Automatic Identification of Age-Appropriate Ratings of Song Lyrics0
A Neural Conversational ModelCode0
Distributional semantics for ontology verification0
Gaze-Enabled Egocentric Video Summarization via Constrained Submodular Maximization0
Trimming a consistent OWL knowledge base, relying on linguistic evidence0
Prepositional Phrase Attachment Problem Revisited: how Verbnet can Help0
The RatioLog Project: Rational Extensions of Logical Reasoning0
Don't Just Listen, Use Your Imagination: Leveraging Visual Common Sense for Non-Visual Tasks0
Recognition of Sarcasms in Tweets Based on Concept Level Sentiment Analysis and Supervised Learning ApproachesCode0
Towards Learning Object Affordance Priors from Technical Texts0
Learning Spatial Knowledge for Text to 3D Scene Generation0
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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