Common Sense Reasoning
257 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
WinoGrande
CommonsenseQA
BIG-bench
VisDial
SWAG
ReCoRD
ARC (AI2 Reasoning Challenge)
CoS-E
PHYRE
Subtasks
Latest papers
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.
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.
