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 91–100 of 158 papers

TitleStatusHype
Enhancing Balanced Graph Edge Partition with Effective Local Search—0
Error Analysis of Shapley Value-Based Model Explanations: An Informative Perspective—0
Experiencing Urban Air Mobility: How Passengers evaluate a simulated flight with an Air Taxi—0
Experimental Contexts Can Facilitate Robust Semantic Property Inference in Language Models, but Inconsistently—0
Explaining deep neural network models for electricity price forecasting with XAI—0
Exploring internal representation of self-supervised networks: few-shot learning abilities and comparison with human semantics and recognition of objects—0
Open-Set Representation Learning through Combinatorial Embedding—0
Open-vocabulary object 6D pose estimation—0
Privacy-Enabled Biometric Search—0
Prototype Recalls for Continual Learning—0
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