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 351–400 of 939 papers

TitleStatusHype
Vision Language Pre-training by Contrastive Learning with Cross-Modal Similarity Regulation—0
Causal Reasoning and Large Language Models: Opening a New Frontier for CausalityCode2
LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale InstructionsCode2
Cluster Flow: how a hierarchical clustering layer make allows deep-NNs more resilient to hacking, more human-like and easily implements relational reasoning—0
Reporting delays: a widely neglected impact factor in COVID-19 forecasts—0
Boosting Theory-of-Mind Performance in Large Language Models via PromptingCode1
A Group-Specific Approach to NLP for Hate Speech DetectionCode0
Pythia: A Suite for Analyzing Large Language Models Across Training and ScalingCode6
Humans in Humans Out: On GPT Converging Toward Common Sense in both Success and Failure—0
BloombergGPT: A Large Language Model for Finance—0
TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs—0
Converging Measures and an Emergent Model: A Meta-Analysis of Human-Automation Trust Questionnaires—0
GrapeQA: GRaph Augmentation and Pruning to Enhance Question-Answering—0
Mind meets machine: Unravelling GPT-4's cognitive psychology—0
FVQA 2.0: Introducing Adversarial Samples into Fact-based Visual Question Answering—0
GPT-4 Technical ReportCode6
Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images—0
LUKE-Graph: A Transformer-based Approach with Gated Relational Graph Attention for Cloze-style Reading Comprehension—0
A primer on getting neologisms from foreign languages to under-resourced languages—0
A Vision for Semantically Enriched Data Science—0
Do Machine Learning Models Learn Statistical Rules Inferred from Data?Code0
LLaMA: Open and Efficient Foundation Language ModelsCode7
HL Dataset: Visually-grounded Description of Scenes, Actions and RationalesCode0
Framework for Certification of AI-Based Systems—0
Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks—0
UKnow: A Unified Knowledge Protocol with Multimodal Knowledge Graph Datasets for Reasoning and Vision-Language Pre-Training—0
Guiding Pretraining in Reinforcement Learning with Large Language ModelsCode1
Benchmarks for Automated Commonsense Reasoning: A Survey—0
Exploring the Benefits of Training Expert Language Models over Instruction TuningCode1
Witscript 2: A System for Generating Improvised Jokes Without Wordplay—0
Planning Automated Driving with Accident Experience Referencing and Common-sense Inferencing—0
Mathematics, word problems, common sense, and artificial intelligence—0
Summarize the Past to Predict the Future: Natural Language Descriptions of Context Boost Multimodal Object Interaction Anticipation—0
Witscript 3: A Hybrid AI System for Improvising Jokes in a Conversation—0
A Theory of Human-Like Few-Shot Learning—0
SparseGPT: Massive Language Models Can Be Accurately Pruned in One-ShotCode4
CHORUS : Learning Canonicalized 3D Human-Object Spatial Relations from Unbounded Synthesized Images—0
Reasoning with Language Model Prompting: A SurveyCode3
Large Language Models are Better Reasoners with Self-VerificationCode1
Despite "super-human" performance, current LLMs are unsuited for decisions about ethics and safety—0
VASR: Visual Analogies of Situation RecognitionCode0
Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUE—0
SimpleMind adds thinking to deep neural networksCode0
Legal Prompting: Teaching a Language Model to Think Like a Lawyer—0
On Utilizing Relationships for Transferable Few-Shot Fine-Grained Object Detection—0
Exploiting Proximity-Aware Tasks for Embodied Social Navigation—0
Layout-aware Dreamer for Embodied Referring Expression GroundingCode1
DiffG-RL: Leveraging Difference between State and Common SenseCode0
A mathematical theory of super-resolution and two-point resolution—0
A Unified Model for Video Understanding and Knowledge Embedding with Heterogeneous Knowledge Graph Dataset—0
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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