Multi Task Language Understanding On Mmlu

Average (%)

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Average (%)	Paper Title	Repository
ALBERT-xxlarge 223M (fine-tuned)	27.1	ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
RoBERTa-base 125M (fine-tuned)	27.9	RoBERTa: A Robustly Optimized BERT Pretraining Approach
GPT-3 175B (5-shot)	43.9	Measuring Massive Multitask Language Understanding
Mixtral 8x7B (5-shot)	70.6	Mixtral of Experts
GPT 3	48.9	UnifiedQA: Crossing Format Boundaries With a Single QA System
Gopher 7.1B (5-shot)	29.5	Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Atlas (5-shot)	47.9	Atlas: Few-shot Learning with Retrieval Augmented Language Models
GPT-3 175B (5-shot)	43.9	Language Models are Few-Shot Learners
GLM-130B	44.8	GLM-130B: An Open Bilingual Pre-trained Model
LLaMA 2 13B (5-shot)	54.8	Llama 2: Open Foundation and Fine-Tuned Chat Models
GPT-NeoX 20B (5-shot)	33.6	GPT-NeoX-20B: An Open-Source Autoregressive Language Model
ds-r1(671b)	87.5	DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
Flan-T5-Base 250M (CoT)	33.7	Scaling Instruction-Finetuned Language Models
Claude Instant 1.1 (5-shot)	73.4	Model Card and Evaluations for Claude Models	-
llama 2(65b)	73.5	Scaling Instruction-Finetuned Language Models
Falcon 40B	57.0	The Falcon Series of Open Language Models	-
LLaMA 65B (fine-tuned)	68.9	LLaMA: Open and Efficient Foundation Language Models
Mistral 7B (5-shot)	60.1	Mistral 7B
Qwen1.5 72B (5-shot)	77.5	-	-
Qwen 7B (5-shot)	56.7	-	-

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