Prompt Engineering

56 papers with code • 10 benchmarks • 9 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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2 papers

Most implemented papers

Learning Transferable Visual Models From Natural Language Supervision

openai/CLIP 26 Feb 2021

State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories.

Multitask Prompted Training Enables Zero-Shot Task Generalization

bigscience-workshop/promptsource ICLR 2022

Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020).

GPT Understands, Too

THUDM/P-tuning 18 Mar 2021

On the SuperGlue benchmark, GPTs achieve comparable and sometimes better performance to similar-sized BERTs in supervised learning.

Learning to Prompt for Vision-Language Models

kaiyangzhou/coop 2 Sep 2021

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 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.

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

zjunlp/DART 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

hazyresearch/ama_prompting 5 Oct 2022

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.

Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models

ucinlp/null-prompts Findings (ACL) 2022

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

kakaobrain/kogpt EMNLP 2021

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

sallymmx/actionclip 17 Sep 2021

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".