Papers with Code 아카이브
Instruction Following
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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Instruction following is the basic task of the model. This task is dedicated to evaluating the ability of the large model to follow human instructions. It is hoped that the model can generate controllable and safe answers.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/safouaneelg/SRT2I
https://github.com/sahil280114/codealpaca
Self-Instruct: Aligning Language Models with Self-Generated Instructions
https://github.com/schwartz-lab-NLP/label-bias
How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources
https://github.com/sihengli99/textbind
TextBind: Multi-turn Interleaved Multimodal Instruction-following in the Wild
https://github.com/skunkworksai/bakllava
https://github.com/snexus/llm-search
https://github.com/sreyan88/reclap
GAMA: A Large Audio-Language Model with Advanced Audio Understanding and Complex Reasoning Abilities
https://github.com/sshh12/multi_token
https://github.com/sunsmarterjie/chatterbox
https://github.com/super5-22/habitat-vis
https://github.com/suryanshkumar/online-joint-depthfusion-and-semantic
https://github.com/tabtoyou/kollava
https://github.com/tanyuqian/cappy
Cappy: Outperforming and Boosting Large Multi-Task LMs with a Small Scorer
https://github.com/teelinsan/camoscio
Self-Instruct: Aligning Language Models with Self-Generated Instructions
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
https://github.com/thudm/glm-4
ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools
https://github.com/thudm/visualglm-6b
https://github.com/tianyic/only_train_once
DistiLLM: Towards Streamlined Distillation for Large Language Models
https://github.com/tml-epfl/long-is-more-for-alignment
https://github.com/togethercomputer/llama-2-7b-32k-instruct
WizardLM: Empowering Large Language Models to Follow Complex Instructions
https://github.com/turbo-llm/turbo-alignment
ASFT: Aligned Supervised Fine-Tuning through Absolute Likelihood
https://github.com/unimodal4reasoning/geox
M3DBench: Let's Instruct Large Models with Multi-modal 3D Prompts
https://github.com/unispac/visual-adversarial-examples-jailbreak-large-language-models
https://github.com/vkartik97/habitat-challenge-two-docker
https://github.com/wangrongsheng/caregpt
How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition
https://github.com/wusize/harmon
https://github.com/xingchensong/s3tokenizer
https://github.com/yangyucheng000/University/tree/main/model-2/blip_2
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-2/jamba
https://github.com/yanyang19/ImPart
Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch
https://github.com/yizhongw/tk-instruct
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
https://github.com/yukw777/videoblip
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.