Common Sense Reasoning
255 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 with no code
The Claude 3 Model Family: Opus, Sonnet, Haiku
We introduce Claude 3, a new family of large multimodal models – Claude 3 Opus, our most capable offering, Claude 3 Sonnet, which provides a combination of skills and speed, and Claude 3 Haiku, our fastest and least expensive model.
SERVAL: Synergy Learning between Vertical Models and LLMs towards Oracle-Level Zero-shot Medical Prediction
Recent development of large language models (LLMs) has exhibited impressive zero-shot proficiency on generic and common sense questions.
Know your exceptions: Towards an Ontology of Exceptions in Knowledge Representation
Defeasible reasoning is a kind of reasoning where some generalisations may not be valid in all circumstances, that is general conclusions may fail in some cases.
Commonsense Ontology Micropatterns
The previously introduced Modular Ontology Modeling methodology (MOMo) attempts to mimic the human analogical process by using modular patterns to assemble more complex concepts.
Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models
For a LLM to be trustworthy, its confidence level should be well-calibrated with its actual performance.
RoboScript: Code Generation for Free-Form Manipulation Tasks across Real and Simulation
To bridge this ``ideal-to-real'' gap, this paper presents \textbf{RobotScript}, a platform for 1) a deployable robot manipulation pipeline powered by code generation; and 2) a code generation benchmark for robot manipulation tasks in free-form natural language.
EvoGrad: A Dynamic Take on the Winograd Schema Challenge with Human Adversaries
Our results emphasize the challenge posed by EvoGrad: Even the best performing LLM, GPT-3. 5, achieves an accuracy of 65. 0% with an average error depth of 7. 2, a stark contrast to human performance of 92.
Multi Task Inverse Reinforcement Learning for Common Sense Reward
One of the challenges in applying reinforcement learning in a complex real-world environment lies in providing the agent with a sufficiently detailed reward function.
Understanding In-Context Learning with a Pelican Soup Framework
In this framework, we introduce (1) the notion of a common sense knowledge base, (2) a general formalism for natural language classification tasks, and the notion of (3) meaning association.
OpenFMNav: Towards Open-Set Zero-Shot Object Navigation via Vision-Language Foundation Models
By leveraging the reasoning and generalizing abilities of foundation models, our method can understand free-form human instructions and perform effective open-set zero-shot navigation in diverse environments.
