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
Generating Question Relevant Captions to Aid Visual Question Answering0
The World in My Mind: Visual Dialog with Adversarial Multi-modal Feature Encoding0
Integrating Semantic Knowledge to Tackle Zero-shot Text ClassificationCode0
Transferable Natural Language Interface to Structured Queries aided by Adversarial Generation0
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
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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