SOTAVerified

General Knowledge

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

Source: BIG-bench

Papers

Showing 301310 of 399 papers

TitleStatusHype
Transformer Based Bengali Chatbot Using General Knowledge Dataset0
TRIM: Token Reduction and Inference Modeling for Cost-Effective Language Generation0
TURNER: The Uncertainty-based Retrieval Framework for Chinese NER0
Understanding Inequality of LLM Fact-Checking over Geographic Regions with Agent and Retrieval models0
Universal Item Tokenization for Transferable Generative Recommendation0
Utilisation d'une base de connaissances de sp\'ecialit\'e et de sens commun pour la simplification de comptes-rendus radiologiques (Radiological text simplification using a general knowledge base)0
Video Question Answering Using CLIP-Guided Visual-Text Attention0
ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints0
Vision-Language Modeling Meets Remote Sensing: Models, Datasets and Perspectives0
Visual Question Answering as Reading Comprehension0
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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