SOTAVerified

General Knowledge

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

Source: BIG-bench

Papers

Showing 2130 of 399 papers

TitleStatusHype
MMA: Multi-Modal Adapter for Vision-Language ModelsCode2
LLM-RG4: Flexible and Factual Radiology Report Generation across Diverse Input ContextsCode2
Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation LearningCode2
A Survey of Personalized Large Language Models: Progress and Future DirectionsCode2
Adapting a Language Model While Preserving its General KnowledgeCode2
Exploring the Potential of Large Language Models (LLMs) in Learning on GraphsCode2
Continual Pre-training of Language ModelsCode2
F-LMM: Grounding Frozen Large Multimodal ModelsCode2
MMRL++: Parameter-Efficient and Interaction-Aware Representation Learning for Vision-Language ModelsCode2
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