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 26–50 of 399 papers

TitleStatusHype
Multi-task Federated Learning with Encoder-Decoder Structure: Enabling Collaborative Learning Across Different Tasks—0
Knowledge Distillation for Underwater Feature Extraction and Matching via GAN-synthesized Images—0
Universal Item Tokenization for Transferable Generative Recommendation—0
Planning Safety Trajectories with Dual-Phase, Physics-Informed, and Transportation Knowledge-Driven Large Language ModelsCode0
Generative Retrieval and Alignment Model: A New Paradigm for E-commerce Retrieval—0
Understanding Inequality of LLM Fact-Checking over Geographic Regions with Agent and Retrieval models—0
A Self-Supervised Learning of a Foundation Model for Analog Layout Design Automation—0
Effective Skill Unlearning through Intervention and AbstentionCode0
Distilling Stereo Networks for Performant and Efficient Leaner NetworksCode0
LoRASculpt: Sculpting LoRA for Harmonizing General and Specialized Knowledge in Multimodal Large Language Models—0
How Robust Are Router-LLMs? Analysis of the Fragility of LLM Routing CapabilitiesCode0
Disentangling Fine-Tuning from Pre-Training in Visual Captioning with Hybrid Markov LogicCode0
Proceedings of the ISCA/ITG Workshop on Diversity in Large Speech and Language Models—0
Foundation X: Integrating Classification, Localization, and Segmentation through Lock-Release Pretraining Strategy for Chest X-ray AnalysisCode0
AnomalyPainter: Vision-Language-Diffusion Synergy for Zero-Shot Realistic and Diverse Industrial Anomaly Synthesis—0
Semi-Supervised Medical Image Segmentation via Knowledge Mining from Large Models—0
FuseChat-3.0: Preference Optimization Meets Heterogeneous Model FusionCode1
Keeping Yourself is Important in Downstream Tuning Multimodal Large Language ModelCode2
AILS-NTUA at SemEval-2025 Task 4: Parameter-Efficient Unlearning for Large Language Models using Data Chunking—0
Evaluating Polish linguistic and cultural competency in large language models—0
GeoEdit: Geometric Knowledge Editing for Large Language Models—0
Sculpting [CLS] Features for Pre-Trained Model-Based Class-Incremental Learning—0
Mol-LLaMA: Towards General Understanding of Molecules in Large Molecular Language Model—0
Sens-Merging: Sensitivity-Guided Parameter Balancing for Merging Large Language Models—0
A Survey of Personalized Large Language Models: Progress and Future DirectionsCode2
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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