https://github.com/MindCode-4/code-1/tree/main/vipllava
ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts
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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.
ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts
ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts
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Learning Transferable Visual Models From Natural Language Supervision
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Learning Transferable Visual Models From Natural Language Supervision
A Comparison of Prompt Engineering Techniques for Task Planning and Execution in Service Robotics
Learning Transferable Visual Models From Natural Language Supervision
Learning Transferable Visual Models From Natural Language Supervision
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.