Prompt Engineering
201 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.
Benchmarks
These leaderboards are used to track progress in Prompt Engineering
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Libraries
Use these libraries to find Prompt Engineering models and implementationsDatasets
ImageNet
UCF101
Oxford 102 Flower
Stanford Cars
DTD
Food-101
Caltech-101
EuroSAT
FGVC-Aircraft
ImageNet-R
Most implemented papers
Learning Transferable Visual Models From Natural Language Supervision
openai/CLIP
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State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories.
Learning to Prompt for Vision-Language Models
kaiyangzhou/coop
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Large pre-trained vision-language models like CLIP have shown great potential in learning representations that are transferable across a wide range of downstream tasks.
Conditional Prompt Learning for Vision-Language Models
kaiyangzhou/coop
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CVPR 2022
With the rise of powerful pre-trained vision-language models like CLIP, it becomes essential to investigate ways to adapt these models to downstream datasets.
GPT Understands, Too
THUDM/P-tuning
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Prompting a pretrained language model with natural language patterns has been proved effective for natural language understanding (NLU).
Multitask Prompted Training Enables Zero-Shot Task Generalization
Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020).
Visual Prompt Tuning
KMnP/vpt
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The current modus operandi in adapting pre-trained models involves updating all the backbone parameters, ie, full fine-tuning.
Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners
zjunlp/DART
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ICLR 2022
Large-scale pre-trained language models have contributed significantly to natural language processing by demonstrating remarkable abilities as few-shot learners.
Ask Me Anything: A simple strategy for prompting language models
Prompting is a brittle process wherein small modifications to the prompt can cause large variations in the model predictions, and therefore significant effort is dedicated towards designing a painstakingly "perfect prompt" for a task.
GPT Takes the Bar Exam
Nearly all jurisdictions in the United States require a professional license exam, commonly referred to as "the Bar Exam," as a precondition for law practice.
MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
Multimodal Large Language Model (MLLM) relies on the powerful LLM to perform multimodal tasks, showing amazing emergent abilities in recent studies, such as writing poems based on an image.
