Prompt Engineering
227 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
Latest papers
Easy Problems That LLMs Get Wrong
We introduce a comprehensive Linguistic Benchmark designed to evaluate the limitations of Large Language Models (LLMs) in domains such as logical reasoning, spatial intelligence, and linguistic understanding, among others.
ORLM: Training Large Language Models for Optimization Modeling
We apply the data from OR-Instruct to various open-source LLMs of 7b size (termed as ORLMs), resulting in a significantly improved capability for optimization modeling.
Optimizing Large Language Models for OpenAPI Code Completion
BohdanPetryshyn/code-llama-fim-fine-tuning
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Recent advancements in Large Language Models (LLMs) and their utilization in code generation tasks have significantly reshaped the field of software development.
Detection and Positive Reconstruction of Cognitive Distortion sentences: Mandarin Dataset and Evaluation
In this study, we emphasize the theoretical foundation for the Positive Reconstruction Framework, grounded in broaden-and-build theory.
A Lost Opportunity for Vision-Language Models: A Comparative Study of Online Test-time Adaptation for Vision-Language Models
mariodoebler/test-time-adaptation
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Through a systematic exploration of prompt-based techniques and existing test-time adaptation methods, the study aims to enhance the adaptability and robustness of vision-language models in diverse real-world scenarios.
E2TP: Element to Tuple Prompting Improves Aspect Sentiment Tuple Prediction
Generative approaches have significantly influenced Aspect-Based Sentiment Analysis (ABSA), garnering considerable attention.
Exploring the Capabilities of Large Multimodal Models on Dense Text
To further explore the capabilities of LMM in complex text tasks, we propose the DT-VQA dataset, with 170k question-answer pairs.
Can We Use Large Language Models to Fill Relevance Judgment Holes?
Incomplete relevance judgments limit the re-usability of test collections.
CityLLaVA: Efficient Fine-Tuning for VLMs in City Scenario
alibaba/aicity2024_track2_aliopentrek_cityllava
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In the vast and dynamic landscape of urban settings, Traffic Safety Description and Analysis plays a pivotal role in applications ranging from insurance inspection to accident prevention.
Towards A Human-in-the-Loop LLM Approach to Collaborative Discourse Analysis
LLMs have demonstrated proficiency in contextualizing their outputs using human input, often matching or beating human-level performance on a variety of tasks.
