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 1120 of 158 papers

TitleStatusHype
SAFE: Slow and Fast Parameter-Efficient Tuning for Continual Learning with Pre-Trained ModelsCode1
Happy: A Debiased Learning Framework for Continual Generalized Category DiscoveryCode1
A Language Model's Guide Through Latent SpaceCode1
Online Task-Free Continual Generative and Discriminative Learning via Dynamic Cluster MemoryCode1
Language-Informed Visual Concept LearningCode1
DreamCreature: Crafting Photorealistic Virtual Creatures from ImaginationCode1
Towards Open-Ended Visual Recognition with Large Language ModelCode1
OV-VG: A Benchmark for Open-Vocabulary Visual GroundingCode1
Link-Context Learning for Multimodal LLMsCode1
CroSSL: Cross-modal Self-Supervised Learning for Time-series through Latent MaskingCode1
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