Common Sense Reasoning
261 papers with code • 31 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.
Libraries
Use these libraries to find Common Sense Reasoning models and implementationsDatasets
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Most implemented papers
DeBERTa: Decoding-enhanced BERT with Disentangled Attention
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
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
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.
PaLM: Scaling Language Modeling with Pathways
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
We then propose to search for the optimal per-channel scaling that protects the salient weights by observing the activation, not weights.
The "something something" video database for learning and evaluating visual common sense
Neural networks trained on datasets such as ImageNet have led to major advances in visual object classification.
Temporal Relational Reasoning in Videos
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
We show that instruction tuning -- finetuning language models on a collection of tasks described via instructions -- substantially improves zero-shot performance on unseen tasks.
DKN: Deep Knowledge-Aware Network for News Recommendation
To solve the above problems, in this paper, we propose a deep knowledge-aware network (DKN) that incorporates knowledge graph representation into news recommendation.