SOTAVerified

General Knowledge

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

Source: BIG-bench

Papers

Showing 301325 of 399 papers

TitleStatusHype
Autonomous Intelligent Software Development0
WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language ModelsCode0
Knowledge-aware Neural Collective Matrix Factorization for Cross-domain Recommendation0
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 Games0
Low Resource Style Transfer via Domain Adaptive Meta Learning0
PASH at TREC 2021 Deep Learning Track: Generative Enhanced Model for Multi-stage Ranking0
Knowledgebra: An Algebraic Learning Framework for Knowledge Graph0
TOV: The Original Vision Model for Optical Remote Sensing Image Understanding via Self-supervised Learning0
Hierarchical Inductive Transfer for Continual Dialogue Learning0
KMIR: A Benchmark for Evaluating Knowledge Memorization, Identification and Reasoning Abilities of Language Models0
ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints0
TURNER: The Uncertainty-based Retrieval Framework for Chinese NER0
Low Resource Style Transfer via Domain Adaptive Meta Learning0
Knowledge Matters: Radiology Report Generation with General and Specific Knowledge0
Applying SoftTriple Loss for Supervised Language Model Fine Tuning0
Hierarchical Inductive Transfer for Continual Dialogue Learning0
DAML-ST5: Low Resource Style Transfer via Domain Adaptive Meta Learning0
GFDC: Graph Function Dependence for Logically Consistent Dialogue Response Beyond Persona Data0
KALA: Knowledge-Augmented Language Model Adaptation0
Transformer Based Bengali Chatbot Using General Knowledge Dataset0
Successive POI Recommendation via Brain-inspired Spatiotemporal Aware Representation0
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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