Prompt Engineering
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Prompt engineering is the process of designing and refining the prompts used to generate text from language models, such as GPT-3 or similar models. The goal of prompt engineering is to improve the quality and relevance of the generated text by carefully crafting the prompts to elicit the desired responses from the model.
Prompt engineering involves several steps, including selecting the appropriate model architecture and parameters, designing the prompt format and structure, selecting the appropriate task and training data, and fine-tuning the model using the selected prompt and data.
Prompt engineering is a crucial step in the development of language models, as it can greatly influence the quality and effectiveness of the model's responses. By carefully designing and refining the prompts used to generate text, researchers and developers can improve the accuracy and relevance of the model's output, making it more useful for a wide range of applications, including chatbots, language translation, content creation, and more.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/kareldo/biodex
https://github.com/kenomo/industrial-clip
https://github.com/klemens-floege/oneprot
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/kohakublueleaf/kgen
TIPO: Text to Image with Text Presampling for Prompt Optimization
https://github.com/kornia/kornia
https://github.com/ksugar/samapi
https://github.com/kynkaat/role-of-imagenet-classes-in-fid
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/lancasterli/refsam
https://github.com/lastmile-ai/aiconfig
https://github.com/leolee99/CLIP_ITM
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/li-qingyun/sam-mmrotate
https://github.com/linjieli222/hero_video_feature_extractor
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/liucongg/chatglm-finetuning
https://github.com/liuxiaoyu1104/smartcontrol
IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models
https://github.com/lukasvierling/dynamicgptswarm
https://github.com/lunaproject22/rpa
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/madrylab/pretraining-distribution-shift-robustness
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/magicyang1573/llm-hardware-test-generation
https://github.com/mainaksingha01/applenet
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/mainaksingha01/odg-clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/martinsbruveris/tensorflow-image-models
https://github.com/medicl-vu/prism-placenta
PRISM: A Promptable and Robust Interactive Segmentation Model with Visual Prompts
https://github.com/mertyg/post-hoc-cbm
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/michi-3000/eyeclip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/microsoft/VLM-Video-Action-Localization
Open-Vocabulary Action Localization with Iterative Visual Prompting
https://github.com/microsoft/llmail-inject-challenge
Defending Against Indirect Prompt Injection Attacks With Spotlighting
https://github.com/microsoft/promptbase
Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine
https://github.com/microsoft/promptwizard
https://github.com/mindspore-lab/mindone
IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models
https://github.com/ml-jku/cloob
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/mlbio-epfl/turtle
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/mlfoundations/open_clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/mlvlab/dapt
https://github.com/moein-shariatnia/OpenAI-CLIP
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/nahidalam/open_clip
Learning Transferable Visual Models From Natural Language Supervision
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.