https://github.com/ma-labo/promptcharm
공식 코드PromptCharm: Text-to-Image Generation through Multi-modal Prompting and Refinement
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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.
PromptCharm: Text-to-Image Generation through Multi-modal Prompting and Refinement
3D Part Segmentation via Geometric Aggregation of 2D Visual Features
DNAHLM -- DNA sequence and Human Language mixed large language Model
Web Archives Metadata Generation with GPT-4o: Challenges and Insights
The Benefits of a Concise Chain of Thought on Problem-Solving in Large Language Models
The Effect of Sampling Temperature on Problem Solving in Large Language Models
Enhancing CLIP with GPT-4: Harnessing Visual Descriptions as Prompts
PRISM: A Promptable and Robust Interactive Segmentation Model with Visual Prompts
Promise:Prompt-driven 3D Medical Image Segmentation Using Pretrained Image Foundation Models
Private Federated Learning using Preference-Optimized Synthetic Data
Beyond Scores: A Modular RAG-Based System for Automatic Short Answer Scoring with Feedback
E2TP: Element to Tuple Prompting Improves Aspect Sentiment Tuple Prediction
Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V
Chain of Natural Language Inference for Reducing Large Language Model Ungrounded Hallucinations
PromptBench: A Unified Library for Evaluation of Large Language Models
MotherNet: Fast Training and Inference via Hyper-Network Transformers
Low-code LLM: Graphical User Interface over Large Language Models
PRE: Vision-Language Prompt Learning with Reparameterization Encoder
PRE: Vision-Language Prompt Learning with Reparameterization Encoder
Toward Evaluative Thinking: Meta Policy Optimization with Evolving Reward Models
Benchmarking Foundation Models on Exceptional Cases: Dataset Creation and Validation
Large (Vision) Language Models are Unsupervised In-Context Learners
Read-only Prompt Optimization for Vision-Language Few-shot Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.