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 376–399 of 399 papers

TitleStatusHype
Adapter-based Approaches to Knowledge-enhanced Language Models -- A Survey—0
SHARP: Unlocking Interactive Hallucination via Stance Transfer in Role-Playing Agents—0
TOV: The Original Vision Model for Optical Remote Sensing Image Understanding via Self-supervised Learning—0
Evaluating Company-specific Biases in Financial Sentiment Analysis using Large Language Models—0
GALA: Generating Animatable Layered Assets from a Single Scan—0
Generating Diverse Q&A Benchmarks for RAG Evaluation with DataMorgana—0
Generative Explore-Exploit: Training-free Optimization of Generative Recommender Systems using LLM Optimizers—0
Generative Meta-Learning for Zero-Shot Relation Triplet Extraction—0
Enhancing Target-unspecific Tasks through a Features Matrix—0
Generative Retrieval and Alignment Model: A New Paradigm for E-commerce Retrieval—0
Enhancing Action Recognition from Low-Quality Skeleton Data via Part-Level Knowledge Distillation—0
GeoEdit: Geometric Knowledge Editing for Large Language Models—0
Enhance Graph Alignment for Large Language Models—0
GFDC: Graph Function Dependence for Logically Consistent Dialogue Response Beyond Persona Data—0
Enabling Autonomic Microservice Management through Self-Learning Agents—0
Towards a Continuous Knowledge Learning Engine for Chatbots—0
Efficient illumination angle self-calibration in Fourier ptychography—0
GOT4Rec: Graph of Thoughts for Sequential Recommendation—0
GRL-Prompt: Towards Knowledge Graph based Prompt Optimization via Reinforcement Learning—0
Dominance-based Rough Set Approach, basic ideas and main trends—0
Domain Specific, Semi-Supervised Transfer Learning for Medical Imaging—0
Does Localization Inform Unlearning? A Rigorous Examination of Local Parameter Attribution for Knowledge Unlearning in Language Models—0
Hierarchical Inductive Transfer for Continual Dialogue Learning—0
Hierarchical Inductive Transfer for Continual Dialogue Learning—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