SOTAVerified

Novel Concepts

Measures the ability of models to uncover an underlying concept that unites several ostensibly disparate entities, which hopefully would not co-occur frequently. This provides a limited test of a model's ability to creatively construct the necessary abstraction to make sense of a situation that it cannot have memorized in training.

Source: BIG-bench

Papers

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Showing 101–110 of 158 papers

TitleStatusHype
A Closer Look at Rehearsal-Free Continual Learning—0
FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic descriptions, and Conceptual Relations—0
Emergence of hierarchical reference systems in multi-agent communicationCode0
Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications—0
Generative Pre-Trained Transformer for Design Concept Generation: An Exploration—0
Few-Shot Novel Concept Learning for Semantic Parsing—0
CoSe-Co: Sentence Conditioned Generative CommonSense Contextualizer for Language Models—0
Designing Rotationally Invariant Neural Networks from PDEs and Variational Methods—0
Decision Making Using Rough Set based Spanning Sets for a Decision System—0
Smoothed Bernstein Online Aggregation for Day-Ahead Electricity Demand Forecasting—0
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