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Prompt Engineering

191 평가 기록 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.

개요는 각 항목의 일부입니다. 위 탭에서 전체 기록을 페이지별로 탐색할 수 있습니다.

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Dataset기록된 모델 예시MetricPaperCode
PromptKD
Harmonic mean: 77.62
논문 코드
PromptKD
Harmonic mean: 97.77
논문 코드
DTD
PromptKD
Harmonic mean: 77.94
논문 코드
MMRL++
Harmonic mean: 91.94
논문 코드
PromptKD
Harmonic mean: 45.17
논문 코드
PromptKD
Harmonic mean: 90.24
논문 코드
PromptKD
Harmonic mean: 97.15
논문 코드
PromptKD
Harmonic mean: 82.60
논문 코드
PromptKD
Harmonic mean: 83.13
논문 코드
PromptKD
Harmonic mean: 86.10
논문 코드
PromptKD
Harmonic mean: 93.05
논문 코드
POMP
Top-1 accuracy %: 51.6
논문 코드

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