Prompt Engineering
197 papers with code • 16 benchmarks • 16 datasets
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.
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Most implemented papers
IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models
Despite the simplicity of our method, an IP-Adapter with only 22M parameters can achieve comparable or even better performance to a fully fine-tuned image prompt model.
Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models
Prompting language models (LMs) with training examples and task descriptions has been seen as critical to recent successes in few-shot learning.
What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers
GPT-3 shows remarkable in-context learning ability of large-scale language models (LMs) trained on hundreds of billion scale data.
ActionCLIP: A New Paradigm for Video Action Recognition
Moreover, to handle the deficiency of label texts and make use of tremendous web data, we propose a new paradigm based on this multimodal learning framework for action recognition, which we dub "pre-train, prompt and fine-tune".
CLIP-Adapter: Better Vision-Language Models with Feature Adapters
Large-scale contrastive vision-language pre-training has shown significant progress in visual representation learning.
Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning
Specifically, vanilla prompt learning may struggle to utilize atypical instances by rote during fully-supervised training or overfit shallow patterns with low-shot data.
MaPLe: Multi-modal Prompt Learning
Pre-trained vision-language (V-L) models such as CLIP have shown excellent generalization ability to downstream tasks.
Large Language Models Are Human-Level Prompt Engineers
By conditioning on natural language instructions, large language models (LLMs) have displayed impressive capabilities as general-purpose computers.
Optimizing Prompts for Text-to-Image Generation
Instead of laborious human engineering, we propose prompt adaptation, a general framework that automatically adapts original user input to model-preferred prompts.
Towards Interpretable Mental Health Analysis with Large Language Models
The latest large language models (LLMs) such as ChatGPT, exhibit strong capabilities in automated mental health analysis.