Papers with Code 아카이브

MMLU

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

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평가표

3

보존된 평가 기록

1

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/thudm/glm-4

ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools

https://github.com/thunlp/clueanchor

Atlas: Few-shot Learning with Retrieval Augmented Language Models

https://github.com/tiger-ai-lab/general-reasoner

General-Reasoner: Advancing LLM Reasoning Across All Domains

https://github.com/tiger-ai-lab/mmlu-pro

MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark

https://github.com/valdecy/pybibx

Gemini: A Family of Highly Capable Multimodal Models

https://github.com/vicentvankor/sun-shine

Qwen2 Technical Report

https://github.com/wenlai-lavine/jola

MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark

https://github.com/wildeval/zeroeval

Are We Done with MMLU?

https://github.com/yangyucheng000/University/tree/main/model-1/qwen2

Qwen2 Technical Report

https://github.com/yangyucheng000/University/tree/main/model-2/chatglm4

ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools

https://github.com/yangyucheng000/University/tree/main/model-3/qwen2_moe

Qwen2 Technical Report

https://github.com/yli-z/ml4h_are_clinical_t5_models_better_for_clinical_text

Scaling Instruction-Finetuned Language Models

https://github.com/yuchenlin/zeroeval

Are We Done with MMLU?

https://github.com/zchuz/timebench

Scaling Instruction-Finetuned Language Models

https://github.com/ziye2chen/LLMs-for-Mathematical-Analysis

Qwen2 Technical Report

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.