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

Showing 101110 of 158 papers

TitleStatusHype
Few-Shot Novel Concept Learning for Semantic Parsing0
CoSe-Co: Sentence Conditioned Generative CommonSense Contextualizer for Language Models0
Designing Rotationally Invariant Neural Networks from PDEs and Variational Methods0
Decision Making Using Rough Set based Spanning Sets for a Decision System0
Smoothed Bernstein Online Aggregation for Day-Ahead Electricity Demand Forecasting0
Open-Set Representation Learning through Combinatorial Embedding0
Neural Concept Formation in Knowledge GraphsCode0
A Clustering-based Framework for Classifying Data StreamsCode0
How Should Agents Ask Questions For Situated Learning? An Annotated Dialogue CorpusCode0
Bridging Few-Shot Learning and Adaptation: New Challenges of Support-Query ShiftCode1
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