SOTAVerified

General Knowledge

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

Source: BIG-bench

Papers

Showing 5160 of 399 papers

TitleStatusHype
BEAMetrics: A Benchmark for Language Generation Evaluation EvaluationCode1
Generative Pre-Training from MoleculesCode1
A New Learning Paradigm for Foundation Model-based Remote Sensing Change DetectionCode1
GeoGalactica: A Scientific Large Language Model in GeoscienceCode1
Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report GenerationCode1
EPVT: Environment-aware Prompt Vision Transformer for Domain Generalization in Skin Lesion RecognitionCode1
HELM: Hyperbolic Large Language Models via Mixture-of-Curvature ExpertsCode1
How Well Do LLMs Handle Cantonese? Benchmarking Cantonese Capabilities of Large Language ModelsCode1
DIAGen: Diverse Image Augmentation with Generative ModelsCode1
Aligning Medical Images with General Knowledge from Large Language ModelsCode1
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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