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
ElecBench: a Power Dispatch Evaluation Benchmark for Large Language ModelsCode1
CurriculumLoc: Enhancing Cross-Domain Geolocalization through Multi-Stage RefinementCode1
A Dual-Space Framework for General Knowledge Distillation of Large Language ModelsCode1
E2Map: Experience-and-Emotion Map for Self-Reflective Robot Navigation with Language ModelsCode1
EPVT: Environment-aware Prompt Vision Transformer for Domain Generalization in Skin Lesion RecognitionCode1
A New Learning Paradigm for Foundation Model-based Remote Sensing Change DetectionCode1
Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report GenerationCode1
Can LLM Generate Culturally Relevant Commonsense QA Data? Case Study in Indonesian and SundaneseCode1
CC-Riddle: A Question Answering Dataset of Chinese Character RiddlesCode1
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