SOTAVerified

General Knowledge

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

Source: BIG-bench

Papers

Showing 131140 of 399 papers

TitleStatusHype
Dominance-based Rough Set Approach, basic ideas and main trends0
Boosting LLM Translation Skills without General Ability Loss via Rationale Distillation0
Domain Specific, Semi-Supervised Transfer Learning for Medical Imaging0
An Energy Ontology for Global City Indicators (ISO 37120)0
Biomedical Large Languages Models Seem not to be Superior to Generalist Models on Unseen Medical Data0
Does Localization Inform Unlearning? A Rigorous Examination of Local Parameter Attribution for Knowledge Unlearning in Language Models0
BinBert: Binary Code Understanding with a Fine-tunable and Execution-aware Transformer0
Dobby: A Conversational Service Robot Driven by GPT-40
DKT: Diverse Knowledge Transfer Transformer for Class Incremental Learning0
Who You Are Matters: Bridging Topics and Social Roles via LLM-Enhanced Logical Recommendation0
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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