SOTAVerified

General Knowledge

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

Source: BIG-bench

Papers

Showing 361370 of 399 papers

TitleStatusHype
Specifying Conceptual Models Using Restricted Natural Language0
Learning to Specialize with Knowledge Distillation for Visual Question Answering0
Visual Question Answering as Reading Comprehension0
Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering0
Explicit Utilization of General Knowledge in Machine Reading Comprehension0
Straight to the Facts: Learning Knowledge Base Retrieval for Factual Visual Question Answering0
Controversy Rules - Discovering Regions Where Classifiers (Dis-)Agree Exceptionally0
Knowledge Representation and Extraction at Scale0
Luminoso at SemEval-2018 Task 10: Distinguishing Attributes Using Text Corpora and Relational KnowledgeCode0
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
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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