Text-to-Text Model (T2T) Prompt Engineering Task
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A Text-to-Text Model (T2T) Prompt Engineering Task is a prompt engineering task that requires the creation of a text-to-text prompt (for text-to-text model) to solve a prompt-based text-to-text model inference task.
- Context:
- It can range from being Manual T2T Prompt Engineering (by a T2T prompt engineer) to being Automated T2T Prompt Engineering.
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- Example(s):
- Question-answering prompting: To perform question-answering tasks, a prompt might include a natural language question, along with any relevant context, such as a passage of text. For example, a prompt might ask "What is the capital of France?" and provide the context of a Wikipedia article about France.
- Language generation prompting: To generate natural language text, a prompt might include a starting sentence or phrase, along with any additional context or constraints. For example, a prompt might start with the phrase "In a world where...", and specify a particular genre or style of writing.
- Sentiment analysis prompting: To perform sentiment analysis tasks, a prompt might include a natural language text, along with a specific question or goal, such as "What is the sentiment of this customer review?"
- Text classification prompting: To perform text classification tasks, a prompt might include a natural language text, along with a set of predefined categories or labels. For example, a prompt might ask "Which of the following categories does this text belong to: Politics, Entertainment, Sports, or Business?"
- Named entity recognition prompting: To perform named entity recognition tasks, a prompt might include a natural language text, along with a specific question or goal, such as "Identify all the people mentioned in this news article."
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- Counter-Example(s):
- See: Prompt, Prompt-based Learning, Language Model, Train of Thought, Few-Shot Learning.
References
2023
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- ... In this context, prompt engineering refers to the process of designing effective prompts or queries that enable NLP models to perform specific tasks. These prompts are often designed in a way that allows the user to specify their task in natural language.
Here are some examples of prompt engineering in NLP:
- Question-answering prompts: To perform question-answering tasks, a prompt might include a natural language question, along with any relevant context, such as a passage of text. For example, a prompt might ask "What is the capital of France?" and provide the context of a Wikipedia article about France.
- Language generation prompts: To generate natural language text, a prompt might include a starting sentence or phrase, along with any additional context or constraints. For example, a prompt might start with the phrase "In a world where...", and specify a particular genre or style of writing.
- Sentiment analysis prompts: To perform sentiment analysis tasks, a prompt might include a natural language text, along with a specific question or goal, such as "What is the sentiment of this customer review?"
- Text classification prompts: To perform text classification tasks, a prompt might include a natural language text, along with a set of predefined categories or labels. For example, a prompt might ask "Which of the following categories does this text belong to: Politics, Entertainment, Sports, or Business?"
- Named entity recognition prompts: To perform named entity recognition tasks, a prompt might include a natural language text, along with a specific question or goal, such as "Identify all the people mentioned in this news article."
- In each of these examples, the prompt is designed to guide the NLP model towards the specific task at hand, while allowing the user to specify their task in natural language
- ... In this context, prompt engineering refers to the process of designing effective prompts or queries that enable NLP models to perform specific tasks. These prompts are often designed in a way that allows the user to specify their task in natural language.