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 5160 of 158 papers

TitleStatusHype
L3DMC: Lifelong Learning using Distillation via Mixed-Curvature SpaceCode0
Multi-level Semantic Feature Augmentation for One-shot LearningCode0
Deep Compositional Captioning: Describing Novel Object Categories without Paired Training DataCode0
Emergence of hierarchical reference systems in multi-agent communicationCode0
Decoupled Novel Object CaptionerCode0
A Provable Defense for Deep Residual NetworksCode0
How Should Agents Ask Questions For Situated Learning? An Annotated Dialogue CorpusCode0
Continual Zero-Shot Learning through Semantically Guided Generative Random WalksCode0
Beneath Surface Similarity: Large Language Models Make Reasonable Scientific Analogies after Structure AbductionCode0
A Clustering-based Framework for Classifying Data StreamsCode0
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