SOTAVerified

General Knowledge

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

Source: BIG-bench

Papers

Showing 3140 of 399 papers

TitleStatusHype
HELM: Hyperbolic Large Language Models via Mixture-of-Curvature ExpertsCode1
A General Knowledge Injection Framework for ICD CodingCode1
A Dual-Space Framework for General Knowledge Distillation of Large Language ModelsCode1
FuseChat-3.0: Preference Optimization Meets Heterogeneous Model FusionCode1
Super-class guided Transformer for Zero-Shot Attribute ClassificationCode1
RAG with Differential PrivacyCode1
SAME: Learning Generic Language-Guided Visual Navigation with State-Adaptive Mixture of ExpertsCode1
SAFE: Slow and Fast Parameter-Efficient Tuning for Continual Learning with Pre-Trained ModelsCode1
Point-PRC: A Prompt Learning Based Regulation Framework for Generalizable Point Cloud AnalysisCode1
DA-Ada: Learning Domain-Aware Adapter for Domain Adaptive Object DetectionCode1
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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