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 6170 of 158 papers

TitleStatusHype
Subspace Distillation for Continual LearningCode0
Beneath Surface Similarity: Large Language Models Make Reasonable Scientific Analogies after Structure AbductionCode0
Benchmarking the human brain against computational architectures0
DRPT: Disentangled and Recurrent Prompt Tuning for Compositional Zero-Shot Learning0
Large-scale Pre-trained Models are Surprisingly Strong in Incremental Novel Class DiscoveryCode1
IFSeg: Image-free Semantic Segmentation via Vision-Language ModelCode1
Strategy Synthesis in Markov Decision Processes Under Limited Sampling Access0
WDiscOOD: Out-of-Distribution Detection via Whitened Linear Discriminant AnalysisCode0
LEDetection: A Simple Framework for Semi-Supervised Few-Shot Object DetectionCode1
Influence zones for continuous beam systems0
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