https://github.com/tmlr-group/wca
공식 코드Visual-Text Cross Alignment: Refining the Similarity Score in 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.
Visual-Text Cross Alignment: Refining the Similarity Score in Vision-Language Models
The Prompt Report: A Systematic Survey of Prompting Techniques
The Prompt Report: A Systematic Survey of Prompting Techniques
The Prompt Report: A Systematic Survey of Prompting Techniques
EconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic Activities
Cheap Learning: Maximising Performance of Language Models for Social Data Science Using Minimal Data
Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models
Text2Cohort: Facilitating Intuitive Access to Biomedical Data with Natural Language Cohort Discovery
Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective
MatSAM: Efficient Extraction of Microstructures of Materials via Visual Large Model
Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RL
Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer
VidProM: A Million-scale Real Prompt-Gallery Dataset for Text-to-Video Diffusion Models
Can Large Language Models Automatically Score Proficiency of Written Essays?
Solving and Generating NPR Sunday Puzzles with Large Language Models
Enhancing Reasoning to Adapt Large Language Models for Domain-Specific Applications
A comparison of Human, GPT-3.5, and GPT-4 Performance in a University-Level Coding Course
EarthMarker: A Visual Prompting Multi-modal Large Language Model for Remote Sensing
How Interpretable are Reasoning Explanations from Prompting Large Language Models?
Sugar-Coated Poison: Benign Generation Unlocks LLM Jailbreaking
Towards Ambiguity-Free Spatial Foundation Model: Rethinking and Decoupling Depth Ambiguity
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.