https://github.com/amazon-science/pixels
공식 코드PIXELS: Progressive Image Xemplar-based Editing with Latent Surgery
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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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
PIXELS: Progressive Image Xemplar-based Editing with Latent Surgery
Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition
OffensiveLang: A Community Based Implicit Offensive Language Dataset
No Token Left Behind: Explainability-Aided Image Classification and Generation
Breaking the Ceiling: Exploring the Potential of Jailbreak Attacks through Expanding Strategy Space
Parameter-efficient Prompt Learning for 3D Point Cloud Understanding
Prompt-based Distribution Alignment for Unsupervised Domain Adaptation
Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation
Improving Large Language Models for Clinical Named Entity Recognition via Prompt Engineering
BigBIO: A Framework for Data-Centric Biomedical Natural Language Processing
Multitask Prompted Training Enables Zero-Shot Task Generalization
Multitask Prompted Training Enables Zero-Shot Task Generalization
Enhancing the Reasoning Capabilities of Small Language Models via Solution Guidance Fine-Tuning
Enhance Reasoning for Large Language Models in the Game Werewolf
MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
A Zero-Shot LLM Framework for Automatic Assignment Grading in Higher Education
Self-Augmented In-Context Learning for Unsupervised Word Translation
ORLM: A Customizable Framework in Training Large Models for Automated Optimization Modeling
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.