SOTAVerified

General Knowledge

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

Source: BIG-bench

Papers

Showing 6170 of 399 papers

TitleStatusHype
See Through Their Minds: Learning Transferable Neural Representation from Cross-Subject fMRICode1
MedSafetyBench: Evaluating and Improving the Medical Safety of Large Language ModelsCode1
Can LLM Generate Culturally Relevant Commonsense QA Data? Case Study in Indonesian and SundaneseCode1
OMGEval: An Open Multilingual Generative Evaluation Benchmark for Large Language ModelsCode1
Pre-training and Diagnosing Knowledge Base Completion ModelsCode1
The Unreasonable Effectiveness of Easy Training Data for Hard TasksCode1
Generic Knowledge Boosted Pre-training For Remote Sensing ImagesCode1
GeoGalactica: A Scientific Large Language Model in GeoscienceCode1
Time Travelling Pixels: Bitemporal Features Integration with Foundation Model for Remote Sensing Image Change DetectionCode1
VIEScore: Towards Explainable Metrics for Conditional Image Synthesis EvaluationCode1
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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