SOTAVerified

General Knowledge

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

Source: BIG-bench

Papers

Showing 141150 of 399 papers

TitleStatusHype
Hierarchical Inductive Transfer for Continual Dialogue Learning0
GRL-Prompt: Towards Knowledge Graph based Prompt Optimization via Reinforcement Learning0
Bilingual Evaluation of Language Models on General Knowledge in University Entrance Exams with Minimal Contamination0
Distributed Fine-tuning of Language Models on Private Data0
Analysis of Watson's Strategies for Playing Jeopardy!0
Disentangling Knowledge-based and Visual Reasoning by Question Decomposition in KB-VQA0
An Ad-hoc graph node vector embedding algorithm for general knowledge graphs using Kinetica-Graph0
DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning0
Differentially Private Distributed Learning for Language Modeling Tasks0
AdaptGCD: Multi-Expert Adapter Tuning for Generalized Category Discovery0
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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