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 201–225 of 399 papers

TitleStatusHype
KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driven Prompt Learning and Enhanced Cross-Modal Integration—0
KD-DETR: Knowledge Distillation for Detection Transformer with Consistent Distillation Points Sampling—0
Differentially Private Distributed Learning for Language Modeling Tasks—0
Key Factors Affecting European Reactions to AI in European Full and Flawed Democracies—0
Deep Prompt Multi-task Network for Abuse Language Detection—0
K-Link: Knowledge-Link Graph from LLMs for Enhanced Representation Learning in Multivariate Time-Series Data—0
KMIR: A Benchmark for Evaluating Knowledge Memorization, Identification and Reasoning Abilities of Language Models—0
Knowledge-aware Neural Collective Matrix Factorization for Cross-domain Recommendation—0
Knowledgebra: An Algebraic Learning Framework for Knowledge Graph—0
Knowledge Completion for Generics using Guided Tensor Factorization—0
Transaction Logic with (Complex) Events—0
Knowledge Distillation for Underwater Feature Extraction and Matching via GAN-synthesized Images—0
Knowledge Distillation via Instance-level Sequence Learning—0
Data structuring for the ontological modelling of wind energy systems—0
Transferable Natural Language Interface to Structured Queries aided by Adversarial Generation—0
Knowledge Matters: Radiology Report Generation with General and Specific Knowledge—0
DAML-ST5: Low Resource Style Transfer via Domain Adaptive Meta Learning—0
Knowledge Representation and Extraction at Scale—0
KnowRA: Knowledge Retrieval Augmented Method for Document-level Relation Extraction with Comprehensive Reasoning Abilities—0
CoRA: Collaborative Information Perception by Large Language Model's Weights for Recommendation—0
Large Language Models as a Tool for Mining Object Knowledge—0
Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning—0
Laughter During Cooperative and Competitive Games—0
T-Norms Driven Loss Functions for Machine Learning—0
Learning from Natural Language Explanations for Generalizable Entity Matching—0
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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