Papers with Code 아카이브

Stereotypical Bias Analysis

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크: 0
평가표: 1
보존된 평가 기록: 4

코드

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

https://github.com/icalk-nlp/educhat

LLaMA: Open and Efficient Foundation Language Models

https://github.com/kamruzzaman15/prompting-techniques-to-reduce-social-bias-in-llms

Investigating Subtler Biases in LLMs: Ageism, Beauty, Institutional, and Nationality Bias in Generative Models

https://github.com/kayvr/token-hawk

LLaMA: Open and Efficient Foundation Language Models

https://github.com/kbressem/medalpaca

LLaMA: Open and Efficient Foundation Language Models

https://github.com/krafton-ai/korani

LLaMA: Open and Efficient Foundation Language Models

https://github.com/liangyuwang/zo2

OPT: Open Pre-trained Transformer Language Models

https://github.com/llamafamily/llama-chinese

LLaMA: Open and Efficient Foundation Language Models

https://github.com/longhao-chen/aicas2024

LLaMA: Open and Efficient Foundation Language Models

https://github.com/meta-llama/llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/mthcom/hscore-dataset-pruning

OPT: Open Pre-trained Transformer Language Models

https://github.com/nebuly-ai/nebullvm/tree/main/apps/accelerate/chatllama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/ntunlplab/traditional-chinese-alpaca

LLaMA: Open and Efficient Foundation Language Models

https://github.com/ofa-sys/expertllama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/ohadrubin/rpt

LLaMA: Open and Efficient Foundation Language Models

https://github.com/phoebussi/alpaca-cot

LLaMA: Open and Efficient Foundation Language Models

https://github.com/pku-alignment/safe-rlhf

OPT: Open Pre-trained Transformer Language Models

https://github.com/qwopqwop200/GPTQ-for-LLaMa

LLaMA: Open and Efficient Foundation Language Models

https://github.com/replicate/cog_stanford_alpaca

LLaMA: Open and Efficient Foundation Language Models

https://github.com/squeezeailab/squeezellm

LLaMA: Open and Efficient Foundation Language Models

https://github.com/stanfordbdhg/llama.cpp

LLaMA: Open and Efficient Foundation Language Models

https://github.com/tatsu-lab/stanford_alpaca

LLaMA: Open and Efficient Foundation Language Models

https://github.com/teelinsan/camoscio

LLaMA: Open and Efficient Foundation Language Models

https://github.com/vcskaushik/LLMzip

LLaMA: Open and Efficient Foundation Language Models

https://github.com/xiaoman-zhang/PMC-VQA

LLaMA: Open and Efficient Foundation Language Models

https://github.com/xiuyu0000/new_papers_codes/blob/main/opt/opt.py

OPT: Open Pre-trained Transformer Language Models

https://github.com/xusenlinzy/api-for-open-llm

LLaMA: Open and Efficient Foundation Language Models

https://github.com/xvyaward/owq

OPT: Open Pre-trained Transformer Language Models

https://github.com/xzhang97666/alpacare

LLaMA: Open and Efficient Foundation Language Models

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

OPT: Open Pre-trained Transformer Language Models

https://github.com/young-geng/easylm

LLaMA: Open and Efficient Foundation Language Models

https://github.com/yuanmu97/secure-transformer-inference

LLaMA: Open and Efficient Foundation Language Models

https://github.com/zihanzhaosjtu/librisqa

LLaMA: Open and Efficient Foundation Language Models

https://github.com/znhy1024/protoco

OPT: Open Pre-trained Transformer Language Models

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