SOTAVerified

General Knowledge

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

Source: BIG-bench

Papers

Showing 7180 of 399 papers

TitleStatusHype
Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank DecompositionCode1
A General Knowledge Injection Framework for ICD CodingCode1
HYDRA: Model Factorization Framework for Black-Box LLM PersonalizationCode1
KALA: Knowledge-Augmented Language Model AdaptationCode1
Knowledge Prompt-tuning for Sequential RecommendationCode1
CurriculumLoc: Enhancing Cross-Domain Geolocalization through Multi-Stage RefinementCode1
A Comprehensive Evaluation of GPT-4V on Knowledge-Intensive Visual Question AnsweringCode1
CRoFT: Robust Fine-Tuning with Concurrent Optimization for OOD Generalization and Open-Set OOD DetectionCode1
DA-Ada: Learning Domain-Aware Adapter for Domain Adaptive Object DetectionCode1
HELM: Hyperbolic Large Language Models via Mixture-of-Curvature ExpertsCode1
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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