SOTAVerified

General Knowledge

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

Source: BIG-bench

Papers

Showing 101110 of 399 papers

TitleStatusHype
CC-Riddle: A Question Answering Dataset of Chinese Character RiddlesCode1
Seed-Guided Topic Discovery with Out-of-Vocabulary SeedsCode1
DR-Tune: Improving Fine-tuning of Pretrained Visual Models by Distribution Regularization with Semantic CalibrationCode1
KGPT: Knowledge-Grounded Pre-Training for Data-to-Text GenerationCode1
KALA: Knowledge-Augmented Language Model AdaptationCode1
MultiGPrompt for Multi-Task Pre-Training and Prompting on GraphsCode1
SAME: Learning Generic Language-Guided Visual Navigation with State-Adaptive Mixture of ExpertsCode1
A New Learning Paradigm for Foundation Model-based Remote Sensing Change DetectionCode1
Luminoso at SemEval-2018 Task 10: Distinguishing Attributes Using Text Corpora and Relational KnowledgeCode0
Are Large Language Models a Good Replacement of Taxonomies?Code0
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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