https://github.com/zhihelu/ensemble_vlm
공식 코드Beyond Sole Strength: Customized Ensembles for Generalized Vision-Language Models
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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.
Beyond Sole Strength: Customized Ensembles for Generalized Vision-Language Models
Token Coordinated Prompt Attention is Needed for Visual Prompting
Contrastive Prompting Enhances Sentence Embeddings in LLMs through Inference-Time Steering
Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners
Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning
Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning
Beyond Prompt Engineering: Robust Behavior Control in LLMs via Steering Target Atoms
Making Language Models Better Tool Learners with Execution Feedback
Toward Automated Simulation Research Workflow through LLM Prompt Engineering Design
Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization
Dynamic Domains, Dynamic Solutions: DPCore for Continual Test-Time Adaptation
OT-VP: Optimal Transport-guided Visual Prompting for Test-Time Adaptation
Exploring the Transferability of Visual Prompting for Multimodal Large Language Models
IP-Prompter: Training-Free Theme-Specific Image Generation via Dynamic Visual Prompting
An AI Chatbot for Explaining Deep Reinforcement Learning Decisions of Service-oriented Systems
Learning Transferable Visual Models From Natural Language Supervision
Learning Transferable Visual Models From Natural Language Supervision
Learning Transferable Visual Models From Natural Language Supervision
Multitask Prompted Training Enables Zero-Shot Task Generalization
Learning Transferable Visual Models From Natural Language Supervision
Learning Transferable Visual Models From Natural Language Supervision
Learning Transferable Visual Models From Natural Language Supervision
Learning Transferable Visual Models From Natural Language Supervision
ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.