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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 301–350 of 939 papers

TitleStatusHype
Affective Common Sense Knowledge Acquisition for Sentiment Analysis—0
Comprehension Based Question Answering using Bloom's Taxonomy—0
A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics—0
Comparing Apples to Oranges: A Dataset & Analysis of LLM Humour Understanding from Traditional Puns to Topical Jokes—0
A Discourse-Annotated Corpus of Conjoined VPs—0
A Bayesian-Symbolic Approach to Learning and Reasoning for Intuitive Physics—0
CommonsenseQA 2.0: Exposing the Limits of AI through Gamification—0
A Study on Neuro-Symbolic Artificial Intelligence: Healthcare Perspectives—0
Commonsense Ontology Micropatterns—0
A Strong Lexical Matching Method for the Machine Comprehension Test—0
Align, Mask and Select: A Simple Method for Incorporating Commonsense Knowledge into Language Representation Models—0
Hierarchical Relational Inference—0
Common Sense Knowledge, Ontology and Text Mining for Implicit Requirements—0
A Statistical View on Synthetic Aperture Imaging for Occlusion Removal—0
Align-GRAG: Reasoning-Guided Dual Alignment for Graph Retrieval-Augmented Generation—0
Commonsense Knowledge in Wikidata—0
Commonsense Knowledge from Scene Graphs for Textual Environments—0
Assisting human experts in the interpretation of their visual process: A case study on assessing copper surface adhesive potency—0
Heuristic Vision Pre-Training with Self-Supervised and Supervised Multi-Task Learning—0
Common Sense Is All You Need—0
FRIDA to the Rescue! Analyzing Synthetic Data Effectiveness in Object-Based Common Sense Reasoning for Disaster Response—0
Assessment of cognitive characteristics in intelligent systems and predictive ability—0
Common-Sense Bias Modeling for Classification Tasks—0
Aspect Extraction from Product Reviews Using Category Hierarchy Information—0
A design of human-like robust AI machines in object identification—0
HEIE: MLLM-Based Hierarchical Explainable AIGC Image Implausibility Evaluator—0
HGSGNLP at IEST 2018: An Ensemble of Machine Learning and Deep Neural Architectures for Implicit Emotion Classification in Tweets—0
FoundaBench: Evaluating Chinese Fundamental Knowledge Capabilities of Large Language Models—0
Forecasting Social Navigation in Crowded Complex Scenes—0
Commonsense about Human Senses: Labeled Data Collection Processes—0
FirePlace: Geometric Refinements of LLM Common Sense Reasoning for 3D Object Placement—0
Ask Me What You Need: Product Retrieval using Knowledge from GPT-3—0
Handling Multiword Expressions in Causality Estimation—0
COMMA-DEER: COmmon-sense Aware Multimodal Multitask Approach for Detection of Emotion and Emotional Reasoning in Conversations—0
ADEPT: An Adjective-Dependent Plausibility Task—0
Fractional trends and cycles in macroeconomic time series—0
Framework for Certification of AI-Based Systems—0
Free Will Belief as a consequence of Model-based Reinforcement Learning—0
Fine-grained evaluation of Quality Estimation for Machine translation based on a linguistically motivated Test Suite—0
From Blind Solvers to Logical Thinkers: Benchmarking LLMs' Logical Integrity on Faulty Mathematical Problems—0
From Common Sense Reasoning to Neural Network Models through Multiple Preferences: an overview—0
Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMs—0
Hallucination Detection in Foundation Models for Decision-Making: A Flexible Definition and Review of the State of the Art—0
FusionSense: Bridging Common Sense, Vision, and Touch for Robust Sparse-View Reconstruction—0
FVQA 2.0: Introducing Adversarial Samples into Fact-based Visual Question Answering—0
FVQA: Fact-based Visual Question Answering—0
FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering—0
Common Sense Knowledge Learning for Open Vocabulary Neural Reasoning: A First View into Chronic Disease Literature—0
Features of Verb Complements in Co-composition: A case study of Chinese baking verb using Weibo corpus—0
Combining PCFG-LA Models with Dual Decomposition: A Case Study with Function Labels and Binarization—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