SOTAVerified

General Knowledge

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

Source: BIG-bench

Papers

Showing 261270 of 399 papers

TitleStatusHype
Continual Pre-training of Language ModelsCode2
Ten Lessons We Have Learned in the New "Sparseland": A Short Handbook for Sparse Neural Network Researchers0
Adapting a Language Model While Preserving its General KnowledgeCode2
KAER: A Knowledge Augmented Pre-Trained Language Model for Entity Resolution0
Few-Shot Class-Incremental Learning via Class-Aware Bilateral DistillationCode1
DKT: Diverse Knowledge Transfer Transformer for Class Incremental Learning0
PoE: a Panel of Experts for Generalized Automatic Dialogue Assessment0
A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and MultimodalCode3
Efficient Relation-aware Neighborhood Aggregation in Graph Neural Networks via Tensor DecompositionCode0
G-MAP: General Memory-Augmented Pre-trained Language Model for Domain TasksCode0
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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