Generative AI Text-Input Prompt
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An Generative AI Text-Input Prompt is a text-based input provided to a generative AI model.
- Context:
- It can (typically) be associated to a Prompt-based GenAI Model Output.
- It can (typically) be intended to guide the GenAI Model to produce Relevant and Useful Information.
- It can be an output an AI Prompt Writing Task (possibly AI prompt engineering, which can be supported by an AI prompt writing system).
- It can range from being a Text-to-Text AI Prompt to being a Text-to-Image AI Prompt to being a Text-to-Sound AI Prompt.
- It can range from being a Single Word AI Prompt to being a Complete Sentence AI Prompt.
- It can range from being Complex AI Prompt to being Simple AI Prompt.
- It can range from being a Beginner-Level AI Prompt to being an Advanced-Level AI Prompts, based user AI prompting expertise.
- It can range from being Zero-Shot AI Prompt to being One-Shot AI Prompt to being a Few-Shot AI Prompt.
- …
- Example(s):
- Text-to-Text AI Prompt, such as:
- “
Summarize the key themes of Shakespeare's 'Macbeth' in three sentences." - “
Translate the following paragraph from English to Spanish: 'Artificial Intelligence is transforming the tech industry.'"
- “
- Text-to-Image AI Prompt, such as:
- “
Create a digital painting of a futuristic cityscape at sunset, highlighting neon lights and flying cars." - “
Illustrate a serene landscape featuring a waterfall, a wooden bridge, and cherry blossom trees."
- “
- Text-to-Sound AI Prompt, such as:
- “
Generate a soothing melody resembling a gentle river flow combined with soft wind chimes." - “
Compose an upbeat, energetic track suitable for a high-intensity workout session."
- “
- Complete Sentence AI Prompt, such as:
- “
What is the current economic impact of renewable energy sources globally?" - “
Explain the process of photosynthesis in plants."
- “
- ...
- Text-to-Text AI Prompt, such as:
- Counter-Example(s):
- See: Prompt Engineering, Question, Query, Conversational Chatbot, Automated Content Generation, Conversational AI, Language Model Training, Natural Language Understanding, Semantic Search, User Interaction Design.
References
2024
- (Ge, Jing et al., 2024) ⇒ Tao Ge, Hu Jing, Li Dong, Shaoguang Mao, Yan Xia, Xun Wang, Si-Qing Chen, and Furu Wei. (2024). “Extensible Prompts for Language Models on Zero-shot Language Style Customization.” In: Advances in Neural Information Processing Systems, 36.
- QUOTE:
- As natural language prompts’ descriptive capability is limited, there is another branch of research studying continuous prompts (Li and Liang, 2021; Lester et al., 2021; Liu et al., 2021; Han et al., 2022; Hu et al., 2021) for fitting downstream tasks. However, these approaches are mainly for fitting ID task data with little consideration of OOD robustness, which means that their learned continuous prompts can hardly be used for OOD tasks or data.
- Recently, Gal et al. (2022) proposed Textual Inversion in the multimodal context, which learns a virtual token to represent an object from an image and reveals that the learned virtual token can be used in unseen prompts for creative image generation (Kumari et al., 2022). X-Prompt is inspired by Gal et al. (2022), trying to learn OOD robust imaginary words to represent what natural language hardly describes to further expand zero-shot learning capabilities for the LLM, although we find it much more challenging to achieve this in NLP than text2image generation, which motivates us to propose context-augmented learning (CAL). To the best of our knowledge, our work is one of the earliest explorations in this direction in the NLP community.
- We propose X-Prompt, an extensible interface for prompting a large language model beyond natural language. X-Prompt can expand in-context learning capabilities to handle more complex instructions for language model customization and may open up many exciting opportunities, such as creative language generation, patching language models with new knowledge of entities (Zaporojets et al., 2022) and events (Ge et al., 2018), and detoxifying and debiasing in language generation (Welbl et al., 2021), far beyond style customization as demonstrated in this work, approaching advanced interaction between humans and large language models.
- NOTES:
- It introduces Extensible Prompt (X-Prompt), a novel method of instructing LLMs using not only NL but also an extensible vocabulary of imaginary words, aiming to enhance prompt descriptiveness beyond traditional NL capabilities.
- It proposes the concept of registering new imaginary words to enable LLMs to comprehend concepts that are challenging to describe with NL, thus facilitating more descriptive and adaptive prompting mechanisms.
- QUOTE:
2023
- (Notion, 2023) ⇒ https://notion.so/blog/how-to-write-ai-prompts
- NOTE: Key tips for effective prompt writing include:
- Treating the AI model as a conversational partner and using natural language.
- Keeping prompts concise yet detailed enough to convey the request clearly.
- Avoiding negative phrases that might confuse the AI.
- Providing comprehensive context to ensure meaningful responses.
- Assigning specific roles or identities to the AI for tasks (e.g., market analyst) to enhance its response quality.
- Being thorough and detailed in prompts, especially for complex tasks.
- Using clarifying statements to guide the AI away from potential errors.
- Incorporating input-output examples (few-shot examples) to guide the AI's output format.
- NOTE: Key tips for effective prompt writing include:
2023
- chat
- A/An AI Prompt is a Text-Based Input given to an Artificial Intelligence Model to generate a Specific Response or Output.
- This concept might also be referred to as a Query, Instruction, or Command.
- It can be a Single Word or a Complete Sentence.
- It can be a Question or a Statement.
- It can be Context-Specific or General.
- It can be used to initiate a Conversation or follow up on a Previous Message.
- It can guide the AI Model to produce Relevant Information and Useful Information.
- Associated concepts: Natural Language Processing, Language Models, Conversational AI, AI-Generated Text, AI Response, Tokenization.