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
Most implemented papers
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
google-research/bert
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NAACL 2019
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
RoBERTa: A Robustly Optimized BERT Pretraining Approach
pytorch/fairseq
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Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging.
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
huggingface/transformers
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arXiv 2019
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP).
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
google-research/ALBERT
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ICLR 2020
Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks.
Language Models are Few-Shot Learners
By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do.
LLaMA: Open and Efficient Foundation Language Models
facebookresearch/llama
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arXiv 2023
We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters.
A Neural Conversational Model
We find that this straightforward model can generate simple conversations given a large conversational training dataset.
Language Models are Unsupervised Multitask Learners
openai/gpt-2
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Preprint 2019
Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets.
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
We explore how generating a chain of thought -- a series of intermediate reasoning steps -- significantly improves the ability of large language models to perform complex reasoning.
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
Foundation models, now powering most of the exciting applications in deep learning, are almost universally based on the Transformer architecture and its core attention module.
