SOTAVerified

General Knowledge

This task aims to evaluate the ability of a model to answer general-knowledge questions.

Source: BIG-bench

Papers

Showing 8190 of 399 papers

TitleStatusHype
Knowledge Prompt-tuning for Sequential RecommendationCode1
Towards Task Sampler Learning for Meta-LearningCode1
GKD: A General Knowledge Distillation Framework for Large-scale Pre-trained Language ModelCode1
Bert4XMR: Cross-Market Recommendation with Bidirectional Encoder Representations from TransformerCode1
MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic SegmentationCode1
Better Question-Answering Models on a BudgetCode1
EPVT: Environment-aware Prompt Vision Transformer for Domain Generalization in Skin Lesion RecognitionCode1
Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report GenerationCode1
Few-Shot Class-Incremental Learning via Class-Aware Bilateral DistillationCode1
PANDA: Prompt Transfer Meets Knowledge Distillation for Efficient Model AdaptationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Chinchilla-70B (few-shot, k=5)Accuracy94.3Unverified
2Gopher-280B (few-shot, k=5)Accuracy93.9Unverified
3Chinchilla-70B (few-shot, k=5)Accuracy 85.7Unverified
4Gopher-280B (few-shot, k=5)Accuracy 84.8Unverified
5Gopher-280B (few-shot, k=5)Accuracy84.2Unverified
6Gopher-280B (few-shot, k=5)Accuracy 84.1Unverified
7Gopher-280B (few-shot, k=5)Accuracy 83.9Unverified
8Gopher-280B (few-shot, k=5)Accuracy83.3Unverified
9Gopher-280B (few-shot, k=5)Accuracy 81.8Unverified
10Gopher-280B (few-shot, k=5)Accuracy 81Unverified