Prompt Engineering

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.

Benchmarks

Libraries

Datasets

Subtasks

Most implemented papers

Learning Transferable Visual Models From Natural Language Supervision

Content

Learning to Prompt for Vision-Language Models

Multitask Prompted Training Enables Zero-Shot Task Generalization

GPT Understands, Too

Conditional Prompt Learning for Vision-Language Models

Visual Prompt Tuning

Ask Me Anything: A simple strategy for prompting language models

Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners

GPT Takes the Bar Exam

MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models