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 1–25 of 399 papers

TitleStatusHype
Training Compute-Optimal Large Language ModelsCode6
Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric PerspectivesCode5
Long-VITA: Scaling Large Multi-modal Models to 1 Million Tokens with Leading Short-Context AccurayCode3
The Breeze 2 Herd of Models: Traditional Chinese LLMs Based on Llama with Vision-Aware and Function-Calling CapabilitiesCode3
VoiceBench: Benchmarking LLM-Based Voice AssistantsCode3
Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud LearningCode3
Cascade Prompt Learning for Vision-Language Model AdaptationCode3
RAGEval: Scenario Specific RAG Evaluation Dataset Generation FrameworkCode3
Beyond Specialization: Assessing the Capabilities of MLLMs in Age and Gender EstimationCode3
SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and MoreCode3
A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and MultimodalCode3
MMRL++: Parameter-Efficient and Interaction-Aware Representation Learning for Vision-Language ModelsCode2
Keeping Yourself is Important in Downstream Tuning Multimodal Large Language ModelCode2
A Survey of Personalized Large Language Models: Progress and Future DirectionsCode2
LLM-RG4: Flexible and Factual Radiology Report Generation across Diverse Input ContextsCode2
Selective Aggregation for Low-Rank Adaptation in Federated LearningCode2
Prior Knowledge Integration via LLM Encoding and Pseudo Event Regulation for Video Moment RetrievalCode2
F-LMM: Grounding Frozen Large Multimodal ModelsCode2
CoIN: A Benchmark of Continual Instruction tuNing for Multimodel Large Language ModelCode2
CyberMetric: A Benchmark Dataset based on Retrieval-Augmented Generation for Evaluating LLMs in Cybersecurity KnowledgeCode2
Imagine Before Go: Self-Supervised Generative Map for Object Goal NavigationCode2
MMA: Multi-Modal Adapter for Vision-Language ModelsCode2
Exploring the Potential of Large Language Models (LLMs) in Learning on GraphsCode2
Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation LearningCode2
Continual Pre-training of Language ModelsCode2
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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