Common Sense Reasoning
253 papers with code • 24 benchmarks • 52 datasets
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.
Benchmarks
These leaderboards are used to track progress in Common Sense Reasoning
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Libraries
Use these libraries to find Common Sense Reasoning models and implementationsDatasets
CommonsenseQA
WinoGrande
BIG-bench
VisDial
SWAG
ReCoRD
ARC (AI2 Reasoning Challenge)
CoS-E
PHYRE
Subtasks
Latest papers
VLLMs Provide Better Context for Emotion Understanding Through Common Sense Reasoning
In the first stage, we propose prompting VLLMs to generate descriptions in natural language of the subject's apparent emotion relative to the visual context.
AILS-NTUA at SemEval-2024 Task 9: Cracking Brain Teasers: Transformer Models for Lateral Thinking Puzzles
giannispana/ails-ntua-at-semeval-2024-task-9-brainteaser
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In this paper, we outline our submission for the SemEval-2024 Task 9 competition: 'BRAINTEASER: A Novel Task Defying Common Sense'.
Common Sense Enhanced Knowledge-based Recommendation with Large Language Model
ysh-1998/csrec
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Knowledge-based recommendation models effectively alleviate the data sparsity issue leveraging the side information in the knowledge graph, and have achieved considerable performance.
Large Language Models Need Consultants for Reasoning: Becoming an Expert in a Complex Human System Through Behavior Simulation
hakys-a/meow
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In the MEOW framework, simulated data are utilized to train an expert model concentrating ``experience'' about a specific task in each independent time of simulation.
IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models
GPT4V, the best-performing VLM, achieves 62. 99% accuracy (4-shot) on the comprehension task and 49. 7% on the localization task (4-shot and Chain-of-Thought).
Hierarchical Spatial Proximity Reasoning for Vision-and-Language Navigation
18979705623/hspr
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Most Vision-and-Language Navigation (VLN) algorithms tend to make decision errors, primarily due to a lack of visual common sense and insufficient reasoning capabilities.
Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM
Leeroo-AI/mergoo
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We investigate efficient methods for training Large Language Models (LLMs) to possess capabilities in multiple specialized domains, such as coding, math reasoning and world knowledge.
Hybrid Reasoning Based on Large Language Models for Autonomous Car Driving
Large Language Models (LLMs) have garnered significant attention for their ability to understand text and images, generate human-like text, and perform complex reasoning tasks.
MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models
Leeroo-AI/mergoo
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Fine-tuning is often necessary to enhance the adaptability of Large Language Models (LLM) to downstream tasks.
G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
Given a graph with textual attributes, we enable users to `chat with their graph': that is, to ask questions about the graph using a conversational interface.
