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 126–150 of 399 papers

TitleStatusHype
PMoE: Progressive Mixture of Experts with Asymmetric Transformer for Continual Learning—0
Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress?Code1
Prompting Encoder Models for Zero-Shot Classification: A Cross-Domain Study in Italian—0
Can Editing LLMs Inject Harm?Code1
Constructing Enhanced Mutual Information for Online Class-Incremental Learning—0
An Ad-hoc graph node vector embedding algorithm for general knowledge graphs using Kinetica-Graph—0
Prior Knowledge Integration via LLM Encoding and Pseudo Event Regulation for Video Moment RetrievalCode2
Quantized Prompt for Efficient Generalization of Vision-Language ModelsCode0
All Roads Lead to Rome: Unveiling the Trajectory of Recommender Systems Across the LLM Era—0
Microsoft Cloud-based Digitization Workflow with Rich Metadata Acquisition for Cultural Heritage Objects—0
Igea: a Decoder-Only Language Model for Biomedical Text Generation in Italian—0
ElecBench: a Power Dispatch Evaluation Benchmark for Large Language ModelsCode1
SAM-Med3D-MoE: Towards a Non-Forgetting Segment Anything Model via Mixture of Experts for 3D Medical Image Segmentation—0
Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMsCode1
SAFT: Towards Out-of-Distribution Generalization in Fine-Tuning—0
To Forget or Not? Towards Practical Knowledge Unlearning for Large Language ModelsCode1
BAPO: Base-Anchored Preference Optimization for Overcoming Forgetting in Large Language Models Personalization—0
Leveraging Large Language Models for enhanced personalised user experience in Smart Homes—0
Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank DecompositionCode1
Disentangling Knowledge-based and Visual Reasoning by Question Decomposition in KB-VQA—0
CityBench: Evaluating the Capabilities of Large Language Models for Urban TasksCode1
Exploring Safety-Utility Trade-Offs in Personalized Language Models—0
Are Large Language Models a Good Replacement of Taxonomies?Code0
RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference ContentCode0
Avoiding Copyright Infringement via Large Language Model UnlearningCode0
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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