SOTAVerified

General Knowledge

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

Source: BIG-bench

Papers

Showing 91100 of 399 papers

TitleStatusHype
Dual Modality Prompt Tuning for Vision-Language Pre-Trained ModelCode1
Learning with Recoverable ForgettingCode1
CC-Riddle: A Question Answering Dataset of Chinese Character RiddlesCode1
Prompt-aligned Gradient for Prompt TuningCode1
Relphormer: Relational Graph Transformer for Knowledge Graph RepresentationsCode1
Seed-Guided Topic Discovery with Out-of-Vocabulary SeedsCode1
Knowledge Graph Contrastive Learning for RecommendationCode1
KALA: Knowledge-Augmented Language Model AdaptationCode1
BEAMetrics: A Benchmark for Language Generation Evaluation EvaluationCode1
Generative Pre-Training from MoleculesCode1
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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