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
Knowledge graphs for empirical concept retrievalCode0
Should We Really Edit Language Models? On the Evaluation of Edited Language ModelsCode0
Knowledge Distillation for Detection Transformer with Consistent Distillation Points SamplingCode0
Joey NMT: A Minimalist NMT Toolkit for NovicesCode0
Distribution-aware Noisy-label Crack SegmentationCode0
Distilling Stereo Networks for Performant and Efficient Leaner NetworksCode0
Patching as Translation: the Data and the MetaphorCode0
PELMS: Pre-training for Effective Low-Shot Multi-Document SummarizationCode0
What Makes Cryptic Crosswords Challenging for LLMs?Code0
Are Large Language Models a Good Replacement of Taxonomies?Code0
Show:102550
← PrevPage 37 of 40Next →

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