SOTAVerified

General Knowledge

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

Source: BIG-bench

Papers

Showing 251260 of 399 papers

TitleStatusHype
Generative Meta-Learning for Zero-Shot Relation Triplet Extraction0
Better Question-Answering Models on a BudgetCode1
SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and MoreCode3
EPVT: Environment-aware Prompt Vision Transformer for Domain Generalization in Skin Lesion RecognitionCode1
Colo-SCRL: Self-Supervised Contrastive Representation Learning for Colonoscopic Video Retrieval0
Stop Words for Processing Software Engineering Documents: Do they Matter?0
Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report GenerationCode1
Video Question Answering Using CLIP-Guided Visual-Text Attention0
Exploit CAM by itself: Complementary Learning System for Weakly Supervised Semantic Segmentation0
Dive into the Resolution Augmentations and Metrics in Low Resolution Face Recognition: A Plain yet Effective New BaselineCode0
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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