A Comprehensive Introduction to Instruction Fine-Tuning for LLMs

Instruction tuning is a process used to enhance large language models (LLMs) by refining their ability to follow specific instructions. OpenAI’s work on InstructGPT first introduced instruction fine-tuning. 

InstructGPT was trained to follow human instructions better by fine-tuning GPT-3 on datasets where humans rated the model’s responses, which was a major step towards producing ChatGPT.

In this article, you’ll learn about the process of instruction fine-tuning to improve the performance of an existing LLM for your specific use case. You’ll also learn about important metrics that can be used to evaluate the performance of your finetuned LLM and quantify its improvement over the base model you started with.

Table of Contents:

1. Fine-tuning LLMs with Instruction Prompts

2. The Process of Instruction Fine-Tuning

3. Preparing Instruction Data Sets

4. Instruction Fine-Tuning Process

5. Evaluation and Performance Metrics

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1. Fine-tuning LLMs with Instruction Prompts

Large LLMs and foundational models such as GPT3 are capable of identifying instructions contained in a prompt and correctly carrying out zero-shot inference, while others, such as smaller LLMs, may fail to carry out the task. 

For instance, when given the instruction “Translate this sentence to French: ‘Hello, how are you?’”, a capable LLM can generate the correct translation “Bonjour, comment ça va?” without needing to see any examples of similar translations beforehand.

However, smaller LLMs, those with less comprehensive training data, or for more complex tasks LLMs may struggle to perform tasks correctly without guidance. To address this, one-shot and few-shot inference techniques are used, where one or a few examples are included in the prompt to help the model understand the task.

Example: One-Shot Inference

• Prompt: “Translate this sentence to German: ‘Good morning.’ Example: ‘How are you?’ -> ‘Wie geht es dir?’”

• Model Output: “Guten Morgen.”

Here, the model uses the provided example to infer how to translate “Good morning” correctly.

Example: Few-Shot Inference

• Prompt: “Translate the following sentences to French: ‘Goodbye.’ Example: ‘Hello’ -> ‘Bonjour’. ‘Thank you’ -> ‘Merci’.”

• Model Output: “Au revoir.”

By providing a few examples, the model gains a better understanding of the translation task and produces accurate results. Fine-tuning offers a solution by further training a base model using labeled examples to update the weights of the LLM.

2. The Process of Instruction Fine-Tuning

In contrast to pre-training, where you train the LLM using vast amounts of unstructured textual data via self-supervised learning, instruction fine-tuning is a supervised learning process where you use a dataset of labeled examples to update the weights of the LLM. 

The labeled examples are prompt-completion pairs, and the fine-tuning process extends the training of the model to improve its ability to generate good completions for a specific task. 

Example of Instruction Fine-Tuning for Different Tasks:

1. Text Classification:

• Task: Classify the sentiment of movie reviews.

• Prompt: “Classify the sentiment of this review: ‘I absolutely loved this movie! It was fantastic from start to finish.’”

• Completion: “Sentiment: Positive

2. Text Summarization:

• Task: Summarize articles.

• Prompt: “Summarize the following article: ‘The stock market saw unprecedented growth today, with major indices reaching all-time highs. Investors are optimistic about the economic recovery.’”

• Completion: “Summary: Stock market hits all-time highs amid economic recovery optimism.”

3. Translation:

• Task: Translate sentences from English to French.

• Prompt: “Translate this sentence to French: ‘The weather is nice today.’”

• Completion: “Le temps est agréable aujourd’hui.”

4. Question Answering:

• Task: Answer questions based on the given text.

• Prompt: “Read the following passage and answer the question: ‘The Great Wall of China is one of the most famous structures in the world. It was built to protect against invasions.’ Question: Why was the Great Wall of China built?”

• Completion: “The Great Wall of China was built to protect against invasions.”

5. Named Entity Recognition (NER):

• Task: Identify and classify proper names, such as people, organizations, and locations.

• Prompt: “Identify and classify the entities in this sentence: ‘Barack Obama was born in Hawaii and served as the President of the United States.’”

• Completion: “Barack Obama: Person, Hawaii: Location, President of the United States: Title”

Instruction fine-tuning is particularly good at improving a model’s performance on various tasks by using examples that demonstrate how it should respond to specific instructions. Here are three of the most important advantages of instruction fine-tuning:

• Task-Specific Expertise: The model becomes highly proficient at specific tasks by learning from labeled examples directly related to those tasks.

• Improved Accuracy: Fine-tuning significantly enhances the model’s accuracy for the tasks it has been trained on, as it learns from explicit instructions and examples.

• Efficiency in Context Handling: Once fine-tuned, the model does not require multiple examples within the prompt, saving space in the context window for other relevant information.