SOTAVerified

General Knowledge

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

Source: BIG-bench

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 276–300 of 399 papers

TitleStatusHype
Towards Ontology Reshaping for KG Generation with User-in-the-Loop: Applied to Bosch Welding—0
PANDA: Prompt Transfer Meets Knowledge Distillation for Efficient Model AdaptationCode1
Dual Modality Prompt Tuning for Vision-Language Pre-Trained ModelCode1
BinBert: Binary Code Understanding with a Fine-tunable and Execution-aware Transformer—0
Autonomous Intelligent Software Development—0
WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language ModelsCode0
Learning with Recoverable ForgettingCode1
CC-Riddle: A Question Answering Dataset of Chinese Character RiddlesCode1
Knowledge-aware Neural Collective Matrix Factorization for Cross-domain Recommendation—0
Connecting a French Dictionary from the Beginning of the 20th Century to WikidataCode0
Comprehensive Fair Meta-learned Recommender SystemCode0
SciDeBERTa: Learning DeBERTa for Science Technology Documents and Fine-Tuning Information Extraction TasksCode0
Task-Driven and Experience-Based Question Answering Corpus for In-Home Robot Application in the House3D Virtual EnvironmentCode0
Laughter During Cooperative and Competitive Games—0
Prompt-aligned Gradient for Prompt TuningCode1
Low Resource Style Transfer via Domain Adaptive Meta Learning—0
Relphormer: Relational Graph Transformer for Knowledge Graph RepresentationsCode1
PASH at TREC 2021 Deep Learning Track: Generative Enhanced Model for Multi-stage Ranking—0
Seed-Guided Topic Discovery with Out-of-Vocabulary SeedsCode1
Knowledge Graph Contrastive Learning for RecommendationCode1
KALA: Knowledge-Augmented Language Model AdaptationCode1
Knowledgebra: An Algebraic Learning Framework for Knowledge Graph—0
TOV: The Original Vision Model for Optical Remote Sensing Image Understanding via Self-supervised Learning—0
Training Compute-Optimal Large Language ModelsCode6
Hierarchical Inductive Transfer for Continual Dialogue Learning—0
Show:102550
← PrevPage 12 of 16Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Chinchilla-70B (few-shot, k=5)Accuracy94.3—Unverified
2Gopher-280B (few-shot, k=5)Accuracy93.9—Unverified
3Chinchilla-70B (few-shot, k=5)Accuracy 85.7—Unverified
4Gopher-280B (few-shot, k=5)Accuracy 84.8—Unverified
5Gopher-280B (few-shot, k=5)Accuracy84.2—Unverified
6Gopher-280B (few-shot, k=5)Accuracy 84.1—Unverified
7Gopher-280B (few-shot, k=5)Accuracy 83.9—Unverified
8Gopher-280B (few-shot, k=5)Accuracy83.3—Unverified
9Gopher-280B (few-shot, k=5)Accuracy 81.8—Unverified
10Gopher-280B (few-shot, k=5)Accuracy 81—Unverified