4.17. PEFT (Parameter Efficient Fine Tuning)

Undergrad's Guide to LLM Buzzwords: PEFT - Fine-Tuning on a Budget for LLMs

Hey Undergrads! Back in the world of LLMs (Large Language Models), those AI whizzes that can write different creative text formats, translate languages, and might even help you brainstorm ideas (but shhh!). Today, we'll explore PEFT (Parameter-Efficient Fine-Tuning), a technique that helps LLMs learn new skills without needing a complete overhaul – like teaching your old bike new tricks without needing a whole new one!

Imagine This:

• You're a master cyclist who excels at road biking. Now, you want to learn mountain biking. While some skills (balance, pedaling) transfer, you'll need to adapt to new terrain and techniques.

• PEFT is like that adaptation for LLMs. It allows them to leverage their existing knowledge (parameters) from previous training to learn new tasks (mountain biking for the LLM) without requiring a massive amount of additional training data (like needing a whole new bike for mountain biking).

Here's the PEFT Breakdown:

• Learning Efficiently: Unlike traditional fine-tuning, which retrains most of an LLM's parameters, PEFT focuses on adjusting a smaller set of parameters. This makes it more efficient, requiring less computational power and data.
• Transferring Knowledge: PEFT capitalizes on the LLM's existing knowledge base. This "foundation" allows the LLM to learn new skills more quickly by adapting its existing abilities.

Feeling Inspired? Let's See PEFT in Action:

• Mastering Different Writing Styles: Train an LLM on writing different creative text formats like poems and code. Then, use PEFT to fine-tune it for writing technical reports. The LLM's understanding of language structure and grammar from creative writing can be adapted to the specific requirements of technical writing, requiring less additional data compared to full fine-tuning.
• Translating New Languages: Train an LLM on translating between English and French. Then, use PEFT to adapt it for translating Spanish to Italian. The LLM's knowledge of translating Romance languages can be leveraged for the new task, requiring less data compared to training from scratch.

PEFT Prompts: Fine-Tuning Your LLM for New Tricks

Here are two example prompts that showcase Parameter-Efficient Fine-Tuning (PEFT) for Large Language Models (LLMs):

Prompt 1: Generating Different Creative Content Formats (Source Task + Target Task):

• Source Task: Train the LLM on a massive dataset of creative text formats like poems, code snippets, and news articles. This broadens the LLM's understanding of language structure and information processing.
• Target Task: Instruct the LLM to write movie scripts based on short story summaries.

PEFT in Action:

Here, PEFT allows the LLM to leverage its existing knowledge from the source task (understanding creative writing elements like plot, character development, and descriptive language) and apply it to the target task. Through PEFT, the LLM fine-tunes its skills to the specific structure and style of movie scripts, requiring less data compared to training from scratch on movie scripts alone.

Prompt 2: Building a Question-Answering System on a Specific Domain (Source Task + Target Task + Fine-Tuning Data):

• Source Task: Train the LLM on a general question-answering dataset covering various topics. This establishes a foundation in information retrieval and comprehension.
• Target Task: Focus the LLM on answering medical-related questions.
• Fine-Tuning Data: Provide the LLM with a smaller dataset of medical text and question-answer pairs specific to the domain.

PEFT in Action:

Here, PEFT helps the LLM adapt its question-answering skills to the medical domain. The LLM's existing knowledge from the source task allows it to understand the question format and information retrieval process. PEFT then fine-tunes this base using the medical fine-tuning data, enabling it to answer medical questions more accurately. This focused training with PEFT requires less data compared to training on a vast dataset of medical text alone.

These prompts demonstrate how PEFT allows LLMs to become proficient in new tasks by leveraging their existing knowledge from previous training. Remember, the effectiveness of PEFT depends on the relevance of the source and target tasks. The more closely related the tasks are, the more efficient and successful PEFT will be in fine-tuning the LLM.

Important Note: PEFT works best when the new task is related to the LLM's existing skills. The more similar the tasks, the easier it is for the LLM to adapt using PEFT.

So next time you use an LLM, remember the power of PEFT! It's like having a built-in learning optimizer that allows the LLM to acquire new skills efficiently by leveraging its existing knowledge. This makes LLMs more adaptable and versatile tools. (Although, unlike you, LLMs probably won't complain about having to re-learn things!).