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
Most implemented papers
DeBERTa: Decoding-enhanced BERT with Disentangled Attention
microsoft/DeBERTa
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ICLR 2021
Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks.
GPT-4 Technical Report
We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs.
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
studio-ousia/luke
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EMNLP 2020
In this paper, we propose new pretrained contextualized representations of words and entities based on the bidirectional transformer.
mT5: A massively multilingual pre-trained text-to-text transformer
google-research/multilingual-t5
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NAACL 2021
The recent "Text-to-Text Transfer Transformer" (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks.
The "something something" video database for learning and evaluating visual common sense
jayleicn/singularity
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ICCV 2017
Neural networks trained on datasets such as ImageNet have led to major advances in visual object classification.
Temporal Relational Reasoning in Videos
metalbubble/TRN-pytorch
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ECCV 2018
Temporal relational reasoning, the ability to link meaningful transformations of objects or entities over time, is a fundamental property of intelligent species.
Finetuned Language Models Are Zero-Shot Learners
google-research/flan
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ICLR 2022
We show that instruction tuning -- finetuning language models on a collection of tasks described via instructions -- substantially improves zero-shot performance on unseen tasks.
PaLM: Scaling Language Modeling with Pathways
lucidrains/CoCa-pytorch
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Google Research 2022
To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM.
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
Large language models (LLMs) have shown excellent performance on various tasks, but the astronomical model size raises the hardware barrier for serving (memory size) and slows down token generation (memory bandwidth).
Mistral 7B
We introduce Mistral 7B v0. 1, a 7-billion-parameter language model engineered for superior performance and efficiency.
