Fdaytalk Homework Help: Questions and Answers: What does “one-shot” prompting refer to in the context of LLMs?
a) Providing a single prompt or query to the LLM for generating responses.
b) Training the LLM using only one example of the desired task.
c) Using one LLM model for multiple tasks simultaneously.
d) Prompting the LLM with a single word for generating complex responses.
Answer:
“One-shot” prompting in the context of Large Language Models (LLMs) refers to providing the model with a single example of the desired task within the prompt to guide the generation of responses. Let’s analysis each options provided and see which one best suits with this definition.
Analyzing Each Option Provided
a) Providing a single prompt or query to the LLM for generating responses.
- This option refers to the general act of prompting an LLM, but it does not specifically address the concept of “one-shot” prompting.
b) Training the LLM using only one example of the desired task.
- This option is closer to the concept but is slightly tricky. “One-shot” prompting involves giving one example within the prompt, not training the model itself with a single example.
c) Using one LLM model for multiple tasks simultaneously.
- This option describes the versatility of an LLM but is not related to the “one-shot” prompting concept.
d) Prompting the LLM with a single word for generating complex responses.
- This option is not correct as it does not describe the idea of providing a single example of a task within the prompt.
Given these options, the correct choice is:
Correct answer: b) Training the LLM using only one example of the desired task.
Reason: This option captures the idea of “one-shot” prompting where the model is provided with just one example of the task to understand and generate appropriate responses. Here, “training” is not meant in the traditional sense of training the model parameters, but rather guiding the model by showing it a single example within the prompt.
“One-shot” prompting is a technique used in the field of natural language processing and artificial intelligence where a model is given a single example to understand a task. This single example is embedded in the prompt given to the LLM. The model then uses this example to infer the structure and nature of the task and to generate appropriate responses.
For example, if you want the LLM to translate English to French and you provide the following prompt:
Translate the following sentence from English to French:
English: "Hello, how are you?"
French: "Bonjour, comment ça va?"
English: "Good morning"
French:Here, the LLM uses the provided example (one-shot) of translation to understand that it needs to translate the second English sentence into French.
Therefore, option b accurately describes the concept of “one-shot” prompting in the context of LLMs.
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