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 151158 of 158 papers

TitleStatusHype
BOWLL: A Deceptively Simple Open World Lifelong LearnerCode0
Beneath Surface Similarity: Large Language Models Make Reasonable Scientific Analogies after Structure AbductionCode0
Subspace Distillation for Continual LearningCode0
A Robust, Efficient Predictive Safety FilterCode0
Understanding MCMC Dynamics as Flows on the Wasserstein SpaceCode0
Decoupled Novel Object CaptionerCode0
Continual Zero-Shot Learning through Semantically Guided Generative Random WalksCode0
Can Vision Language Models Learn from Visual Demonstrations of Ambiguous Spatial Reasoning?Code0
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