Papers with Code 아카이브
Instruction Following
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/ist-daslab/rosa
https://github.com/jcoreyes/lgpl
https://github.com/jerrywu-code/susgen
https://github.com/jianguanthu/amor
Unlocking Reasoning Potential in Large Langauge Models by Scaling Code-form Planning
https://github.com/jiwanchung/vlis
https://github.com/jondurbin/airoboros
Self-Instruct: Aligning Language Models with Self-Generated Instructions
https://github.com/josejg/instruction_following_eval
https://github.com/jquesnelle/scaled-rope
SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling
https://github.com/jquesnelle/yarn
SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling
https://github.com/junshutang/Make-It-3D
https://github.com/jzhang38/longmamba
LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models
https://github.com/kakaobrain/honeybee
https://github.com/kaushikb258/Habitat_RL
https://github.com/kdr/videorag-mrr2024
https://github.com/kehanlu/DeSTA2
https://github.com/kuleshov-group/llmtune
ModuLoRA: Finetuning 2-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers
https://github.com/kylrth/procedure-generation
https://github.com/lcw99/evolve-instruct
WizardLM: Empowering Large Language Models to Follow Complex Instructions
https://github.com/ledzy/badam
https://github.com/lichang-chen/instructzero
https://github.com/lightaime/camel
Self-Instruct: Aligning Language Models with Self-Generated Instructions
https://github.com/lightblue-tech/multilingual-mt-bench
LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset
https://github.com/lil-lab/chalet
Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction
https://github.com/lil-lab/ciff
Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction
https://github.com/linzhiqiu/clip-flant5
https://github.com/llava-annonymous/llava
https://github.com/llavar/llavar-2
LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding
https://github.com/lllyasviel/omost
Ranni: Taming Text-to-Image Diffusion for Accurate Instruction Following
https://github.com/lntzm/cvpr24track-longvideo
TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding
https://github.com/lucidrains/self-rewarding-lm-pytorch
https://github.com/lunyiliu/logprompt
CoachLM: Automatic Instruction Revisions Improve the Data Quality in LLM Instruction Tuning
https://github.com/lylin0/inditag
https://github.com/magic-research/bubogpt
https://github.com/maomaocun/dllm-cache
https://github.com/megagonlabs/ambignlg
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.