Example of a Structured Fine-Tuning Sample for Machine Translation
A fine-tuning data sample for machine translation can be structured with distinct fields for the instruction, user input, and the expected output. For an English-to-Chinese task, this format would be as follows:
- Instruction: Translate the text from English to Chinese.
- User Input: How’s the weather today?
- Output: 今天天气怎么样？
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Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Example of a Generated Fine-Tuning Sample for Machine Translation
Example of a Structured Fine-Tuning Sample for Machine Translation
A developer is preparing data to train a language model for a French-to-Spanish translation task. They are using the following prompt template and text pair:
Template: Translate the following text from French to Spanish.\n\nFrench: {text}\n\nSpanish: {translation}
Text Pair:
- Source Text (French): "Bonjour, comment ça va ?"
- Target Text (Spanish): "Hola, ¿cómo estás?"
Analyze the options below and select the one that represents a correctly generated fine-tuning sample based on the provided components.
You are preparing a dataset to fine-tune a language model for a machine translation task. Arrange the following actions in the correct chronological order to generate a single, complete fine-tuning sample.
Troubleshooting a Machine Translation Fine-Tuning Sample
Example of a Concatenated Sample for Machine Translation Fine-Tuning
Learn After
A developer is preparing data to teach a model how to translate French legal text into English. Each data sample must be structured with an instruction, an input, and an output. Which of the following examples is structured incorrectly for this task?
Constructing a Machine Translation Fine-Tuning Sample
Evaluating a Fine-Tuning Sample for Machine Translation