SOTAVerified

General Knowledge

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

Source: BIG-bench

Papers

Showing 91100 of 399 papers

TitleStatusHype
FuseChat-3.0: Preference Optimization Meets Heterogeneous Model FusionCode1
Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank DecompositionCode1
Benchmarking Large Language Models for Persian: A Preliminary Study Focusing on ChatGPTCode1
DIAGen: Diverse Image Augmentation with Generative ModelsCode1
Bert4XMR: Cross-Market Recommendation with Bidirectional Encoder Representations from TransformerCode1
Go From the General to the Particular: Multi-Domain Translation with Domain Transformation NetworksCode1
Better Question-Answering Models on a BudgetCode1
Health Index Estimation Through Integration of General Knowledge with Unsupervised LearningCode1
Dual Modality Prompt Tuning for Vision-Language Pre-Trained ModelCode1
CityBench: Evaluating the Capabilities of Large Language Models for Urban TasksCode1
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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