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MMLU

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크

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평가표

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보존된 평가 기록

1

코드

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

https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/baichuan

Baichuan 2: Open Large-scale Language Models

https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/chatglm

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

https://github.com/AlignmentResearch/scaling-llm-robustness-paper

A StrongREJECT for Empty Jailbreaks

https://github.com/QwenLM/Qwen2.5-Omni

Qwen2.5-Omni Technical Report

https://github.com/alon-albalak/online-data-mixing

Efficient Online Data Mixing For Language Model Pre-Training

https://github.com/axiomlab/Cable

The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale

https://github.com/coastalcph/zeroshot_lexglue

Scaling Instruction-Finetuned Language Models

https://github.com/declare-lab/flan-alpaca

Scaling Instruction-Finetuned Language Models

https://github.com/dmis-lab/olaph

Large Language Models Encode Clinical Knowledge

https://github.com/facebookresearch/lingua

DataComp-LM: In search of the next generation of training sets for language models

https://github.com/formulamonks/llm-benchmarker-suite

Scaling Instruction-Finetuned Language Models

https://github.com/gair-nlp/prox

DataDecide: How to Predict Best Pretraining Data with Small Experiments

https://github.com/google-research/flan

Scaling Instruction-Finetuned Language Models

https://github.com/guidance-ai/guidance

ART: Automatic multi-step reasoning and tool-use for large language models

https://github.com/intellabs/fastrag

REPLUG: Retrieval-Augmented Black-Box Language Models

https://github.com/jieyuz2/taskmeanything

DataComp-LM: In search of the next generation of training sets for language models

https://github.com/kapllan/zeroshot_lexglue

Scaling Instruction-Finetuned Language Models

https://github.com/karpathy/llama2.c

Training Compute-Optimal Large Language Models

https://github.com/leondiao0427/seas

Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignment

https://github.com/liano3/RAG-fairness

REPLUG: Retrieval-Augmented Black-Box Language Models

https://github.com/m42-health/med42

Towards Expert-Level Medical Question Answering with Large Language Models

https://github.com/mariushobbhahn/swebench-verified-mini

tinyBenchmarks: evaluating LLMs with fewer examples

https://github.com/microsoft/guidance

ART: Automatic multi-step reasoning and tool-use for large language models

https://github.com/mlfoundations/dclm

DataComp-LM: In search of the next generation of training sets for language models

https://github.com/nkluge-correa/teenytinyllama

Training Compute-Optimal Large Language Models

https://github.com/nvidia/ruler

Make Your LLM Fully Utilize the Context

https://github.com/opennlg/openba-v2

UL2: Unifying Language Learning Paradigms

https://github.com/phixion/phixion

LM2: Large Memory Models

https://github.com/qiaoxiao7282/seft

OwLore: Outlier-weighed Layerwise Sampled Low-Rank Projection for Memory-Efficient LLM Fine-tuning

https://github.com/qwenlm/qwen1.5

Qwen2 Technical Report

https://github.com/ruc-nlpir/flashrag

REPLUG: Retrieval-Augmented Black-Box Language Models

https://github.com/sentient-agi/oml-1.0-fingerprinting

tinyBenchmarks: evaluating LLMs with fewer examples

https://github.com/theoremone/llm-benchmarker-suite

Scaling Instruction-Finetuned Language Models

https://github.com/thudm/chatglm

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

https://github.com/thudm/chatglm3

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

https://github.com/thudm/chatglm4

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

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