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
1MMRL++Harmonic mean91.94Unverified
2PromptKDHarmonic mean89.14Unverified
3HPT++Harmonic mean87.36Unverified
4MMRLHarmonic mean87.21Unverified
5CoPromptHarmonic mean85.84Unverified
6ProMetaRHarmonic mean85.3Unverified
7DePTHarmonic mean84.88Unverified
8HPTHarmonic mean84.82Unverified
9MetaPromptHarmonic mean83.38Unverified
10MaPLeHarmonic mean82.35Unverified