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Multi-task Language Understanding

The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more. https://arxiv.org/pdf/2009.03300.pdf

Papers

Showing 125 of 57 papers

TitleStatusHype
Measuring Hong Kong Massive Multi-Task Language Understanding0
Effectiveness of Zero-shot-CoT in Japanese Prompts0
TUMLU: A Unified and Native Language Understanding Benchmark for Turkic LanguagesCode1
IndicMMLU-Pro: Benchmarking Indic Large Language Models on Multi-Task Language Understanding0
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement LearningCode15
MMLU-CF: A Contamination-free Multi-task Language Understanding BenchmarkCode2
Llama 3 Meets MoE: Efficient UpcyclingCode0
GPT-4o as the Gold Standard: A Scalable and General Purpose Approach to Filter Language Model Pretraining Data0
Reasoning Beyond Bias: A Study on Counterfactual Prompting and Chain of Thought Reasoning0
The Llama 3 Herd of ModelsCode4
Claude 3.5 Sonnet Model Card Addendum0
Breaking the Ceiling of the LLM Community by Treating Token Generation as a Classification for EnsemblingCode2
Hierarchical Prompting Taxonomy: A Universal Evaluation Framework for Large Language Models Aligned with Human Cognitive PrinciplesCode1
MMLU-SR: A Benchmark for Stress-Testing Reasoning Capability of Large Language Models0
MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding BenchmarkCode3
Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLMCode0
The Claude 3 Model Family: Opus, Sonnet, Haiku0
ArabicMMLU: Assessing Massive Multitask Language Understanding in ArabicCode1
Routoo: Learning to Route to Large Language Models EffectivelyCode2
Mixtral of ExpertsCode4
Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General TasksCode2
Gemini: A Family of Highly Capable Multimodal ModelsCode1
The Falcon Series of Open Language Models0
Orca 2: Teaching Small Language Models How to Reason0
MiLe Loss: a New Loss for Mitigating the Bias of Learning Difficulties in Generative Language ModelsCode1
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