SOTAVerified

General Knowledge

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

Source: BIG-bench

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 221–230 of 399 papers

TitleStatusHype
Large Language Models as a Tool for Mining Object Knowledge—0
Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning—0
Laughter During Cooperative and Competitive Games—0
T-Norms Driven Loss Functions for Machine Learning—0
Learning from Natural Language Explanations for Generalizable Entity Matching—0
Learning Knowledge Graphs for Question Answering through Conversational Dialog—0
Learning Physical Common Sense as Knowledge Graph Completion via BERT Data Augmentation and Constrained Tucker Factorization—0
Transfer learning of chaotic systems—0
Learning to Model the World with Language—0
Controversy Rules - Discovering Regions Where Classifiers (Dis-)Agree Exceptionally—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Chinchilla-70B (few-shot, k=5)Accuracy94.3—Unverified
2Gopher-280B (few-shot, k=5)Accuracy93.9—Unverified
3Chinchilla-70B (few-shot, k=5)Accuracy 85.7—Unverified
4Gopher-280B (few-shot, k=5)Accuracy 84.8—Unverified
5Gopher-280B (few-shot, k=5)Accuracy84.2—Unverified
6Gopher-280B (few-shot, k=5)Accuracy 84.1—Unverified
7Gopher-280B (few-shot, k=5)Accuracy 83.9—Unverified
8Gopher-280B (few-shot, k=5)Accuracy83.3—Unverified
9Gopher-280B (few-shot, k=5)Accuracy 81.8—Unverified
10Gopher-280B (few-shot, k=5)Accuracy 81—Unverified