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 126–150 of 399 papers

TitleStatusHype
Patching as Translation: the Data and the MetaphorCode0
Disentangling Fine-Tuning from Pre-Training in Visual Captioning with Hybrid Markov LogicCode0
Pruning neural network models for gene regulatory dynamics using data and domain knowledgeCode0
PELMS: Pre-training for Effective Low-Shot Multi-Document SummarizationCode0
Quantized Prompt for Efficient Generalization of Vision-Language ModelsCode0
Luminoso at SemEval-2018 Task 10: Distinguishing Attributes Using Text Corpora and Relational KnowledgeCode0
DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning ApproachCode0
MM-Eval: A Hierarchical Benchmark for Modern Mongolian Evaluation in LLMsCode0
Avoiding Copyright Infringement via Large Language Model UnlearningCode0
Learning to Understand Phrases by Embedding the DictionaryCode0
ContextFlow++: Generalist-Specialist Flow-based Generative Models with Mixed-Variable Context EncodingCode0
GenKnowSub: Improving Modularity and Reusability of LLMs through General Knowledge SubtractionCode0
Learning to Learn Variational Semantic MemoryCode0
Leveraging Large Language Models for Automated Dialogue AnalysisCode0
Connecting a French Dictionary from the Beginning of the 20th Century to WikidataCode0
Knowledge Distillation for Detection Transformer with Consistent Distillation Points SamplingCode0
Joey NMT: A Minimalist NMT Toolkit for NovicesCode0
Foundation X: Integrating Classification, Localization, and Segmentation through Lock-Release Pretraining Strategy for Chest X-ray AnalysisCode0
Integrating Semantic Knowledge to Tackle Zero-shot Text ClassificationCode0
Commonsense Knowledge in Word Associations and ConceptNetCode0
Towards Difficulty-Agnostic Efficient Transfer Learning for Vision-Language ModelsCode0
Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated LearningCode0
Improving Personalized Search with Regularized Low-Rank Parameter UpdatesCode0
Comprehensive Fair Meta-learned Recommender SystemCode0
Fantastic Gains and Where to Find Them: On the Existence and Prospect of General Knowledge Transfer between Any Pretrained ModelCode0
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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