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Zero-Shot Prompt: Understanding and Leveraging LLM Features
Zero-Shot prompting refers to the ability of large-scale language models, such as GPT-3, for artificial intelligence to perform a specific task without prior specific examples or demos. This function allows artificial intelligence to freely perform various tasks by learning various data.
Example of Zero-Shot prompt?
For example, consider the task of classifying the sentiment of a specific text. If a user enters the sentence "This guidebook is worth reading" without any explanation, the language model classifies it as 'neutral'.
In this process, the artificial intelligence correctly analyzes the emotion of the sentence, even though it has not received specific examples of emotion classification in advance. This demonstrates the core functionality of the Zero-Shot prompt. In other words, solving a problem using only the capabilities of an existing language model without any prior data is called a zero shot.
Advances in Zero-Shot Learning
Instruction tuning presented in the 2022 study <FINETUNED LANGUAGE MODELS ARE ZERO-SHOT LEARNERS> by Jason Wei further improves this feature.
FINETUNED LANGUAGE MODELS ARE ZERO-SHOT LEARNERS.pdf1.6MB
Instruction tuning is the process of making fine adjustments to a data set so that artificial intelligence can more accurately understand and follow the instructions given. In fact, this function is applied to chatGPT's 'Custom instructions' function. This helps the user derive the intended answer in zero shots without requiring the user to issue any special commands or guidance each time.
Reinforcement learning with human feedback (RLHF) also plays an important role, allowing artificial intelligence models to improve and better adapt to human preferences and guidance.
Switch to one-shot prompts
Zero-Shot prompts are powerful, but can have limitations for complex or nuanced tasks. In these cases, it's a good idea to incorporate an example or demo into the prompt, which is called a 'one-shot prompt'. Single-shot prompts allow artificial intelligence to provide more accurate and detailed results.
It may be difficult to say, but it means that when you give an order at once, you have to give it correctly. By suggesting examples and standards. According to the sentiment classifier we talked about earlier, we can do this.
If you proceed properly when entering the prompt at once, you can extract meaningful results even from models with somewhat lower performance, even if they are not models such as GPT-3 or 4. Understanding and utilizing the Zero-Shot Prompt feature of artificial intelligence in this way opens up application possibilities in a variety of fields.
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