SOTAVerified

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.

Papers

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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PromptKDHarmonic mean82.6Unverified
2CoPromptHarmonic mean81.31Unverified
3MMRL++Harmonic mean81.28Unverified
4MMRLHarmonic mean81.2Unverified
5HPT++Harmonic mean81.11Unverified
6DePTHarmonic mean81.06Unverified
7HPTHarmonic mean80.88Unverified
8ProMetaRHarmonic mean80.82Unverified
9MetaPromptHarmonic mean80.62Unverified
10PromptSRCHarmonic mean80.52Unverified