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/nifangbaage/explict-visual-prompt
Explicit Visual Prompting for Universal Foreground Segmentations
https://github.com/nikivanstein/LLaMEA
Optimizing Photonic Structures with Large Language Model Driven Algorithm Discovery
https://github.com/nopperl/clip_arxiv_pmc
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/openbiolink/promptsource
Multitask Prompted Training Enables Zero-Shot Task Generalization
https://github.com/paperspapers/badprompt
Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners
https://github.com/paulosantosneto/unofficial-cot-decoding
https://github.com/philippendres/CellPilot
https://github.com/prabhupad26/100daysofML
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/prapti19/prompt_autohint
AutoHint: Automatic Prompt Optimization with Hint Generation
https://github.com/pseulki/rococo
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/pwc-1/Paper-10/tree/main/sam
https://github.com/pwc-1/Paper-8/tree/main/clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/pwc-1/Paper-9/tree/main/5/vipllava
ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts
https://github.com/ramanakshay/clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/rayat137/VisualPromptGFSS
Visual Prompting for Generalized Few-shot Segmentation: A Multi-scale Approach
https://github.com/realwangjiahao/timereasoner
Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs
https://github.com/redcaps-dataset/redcaps-downloader
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/rinnakk/japanese-clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/ruocwang/mixture-of-prompts
https://github.com/s-a-malik/multi-few
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/sajjjadayobi/CLIPfa
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/salesforce/pb-ovd
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/sberbank-ai/ru-clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/seu-zxj/llm-augmented-mtr
Large Language Models Powered Context-aware Motion Prediction in Autonomous Driving
https://github.com/shivammehta25/clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/shkarupa-alex/tfclip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/shreyansh26/Red-Teaming-Language-Models-with-Language-Models
https://github.com/shunk031/simple-aesthetics-predictor
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/simran-arora/focus
Ask Me Anything: A simple strategy for prompting language models
https://github.com/simran-arora/privacy_fm
Ask Me Anything: A simple strategy for prompting language models
https://github.com/sincerass/mvlpt
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/sithu31296/simple-object-tracking
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/srvcodes/clap4clip
https://github.com/stevekgyang/mentallama
Towards Interpretable Mental Health Analysis with Large Language Models
https://github.com/sunshineluyao/EthosGPT
CultureLLM: Incorporating Cultural Differences into Large Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.