Common Sense Reasoning
254 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
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.
HAZARD Challenge: Embodied Decision Making in Dynamically Changing Environments
umass-foundation-model/hazard
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Recent advances in high-fidelity virtual environments serve as one of the major driving forces for building intelligent embodied agents to perceive, reason and interact with the physical world.
Knowledge Fusion of Large Language Models
fanqiwan/fusellm
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In this paper, we introduce the notion of knowledge fusion for LLMs, aimed at combining the capabilities of existing LLMs and transferring them into a single LLM.
CBVS: A Large-Scale Chinese Image-Text Benchmark for Real-World Short Video Search Scenarios
QQBrowserVideoSearch/CBVS-UniCLIP
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Differently, video covers in short video search scenarios are presented as user-originated contents that provide important visual summaries of videos.
Large Language Models Are Neurosymbolic Reasoners
A wide range of real-world applications is characterized by their symbolic nature, necessitating a strong capability for symbolic reasoning.
Mixtral of Experts
hit-scir/chinese-mixtral-8x7b
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In particular, Mixtral vastly outperforms Llama 2 70B on mathematics, code generation, and multilingual benchmarks.
A Content-Based Novelty Measure for Scholarly Publications: A Proof of Concept
Novelty, akin to gene mutation in evolution, opens possibilities for scholarly advancement.
Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks
wuhy68/parameter-efficient-moe
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Instruction tuning, a successful paradigm, enhances the ability of LLMs to follow natural language instructions and exhibit robust generalization across a wide range of tasks.
Collaborative Synthesis of Patient Records through Multi-Visit Health State Inference
p1nksnow/MSIC
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Furthermore, we propose to generate medical reports to add textual descriptions for each medical event, providing broader applications for synthesized EHR data.
A Semantic Space is Worth 256 Language Descriptions: Make Stronger Segmentation Models with Descriptive Properties
lambert-x/prolab
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Instead of relying solely on category-specific annotations, ProLab uses descriptive properties grounded in common sense knowledge for supervising segmentation models.
