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 301–325 of 399 papers

TitleStatusHype
KMIR: A Benchmark for Evaluating Knowledge Memorization, Identification and Reasoning Abilities of Language Models—0
ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints—0
TURNER: The Uncertainty-based Retrieval Framework for Chinese NER—0
Low Resource Style Transfer via Domain Adaptive Meta Learning—0
Knowledge Matters: Radiology Report Generation with General and Specific Knowledge—0
Applying SoftTriple Loss for Supervised Language Model Fine Tuning—0
Scaling Language Models: Methods, Analysis & Insights from Training GopherCode2
DAML-ST5: Low Resource Style Transfer via Domain Adaptive Meta Learning—0
Hierarchical Inductive Transfer for Continual Dialogue Learning—0
GFDC: Graph Function Dependence for Logically Consistent Dialogue Response Beyond Persona Data—0
KALA: Knowledge-Augmented Language Model Adaptation—0
Transformer Based Bengali Chatbot Using General Knowledge Dataset—0
BEAMetrics: A Benchmark for Language Generation Evaluation EvaluationCode1
Successive POI Recommendation via Brain-inspired Spatiotemporal Aware Representation—0
An Adaptive Deep Learning Framework for Day-ahead Forecasting of Photovoltaic Power Generation—0
Commonsense Knowledge in Word Associations and ConceptNetCode0
Generative Pre-Training from MoleculesCode1
Teaching Uncertainty Quantification in Machine Learning through Use Cases—0
Low-Resource Adaptation of Open-Domain Generative Chatbots—0
Importance-based Neuron Allocation for Multilingual Neural Machine TranslationCode1
Knowledge Distillation via Instance-level Sequence Learning—0
Exploiting Adapters for Cross-lingual Low-resource Speech RecognitionCode0
Explainable Hierarchical Imitation Learning for Robotic Drink Pouring—0
Meta-Inductive Node Classification across Graphs—0
Spirit Distillation: A Model Compression Method with Multi-domain Knowledge Transfer—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