Single Vs Multi-Task LLM Instruction Fine-Tuning

The comparative advantages and challenges of single-task versus multi-task fine-tuning of large language models (LLMs) are explored. The discussion begins with single-task fine-tuning, highlighting its benefits and drawbacks, including the issue of catastrophic forgetting. 

It then transitions to an overview of multitasking fine-tuning, examining both its challenges and potential benefits. The introduction of FLAN models, specifically the FLAN-T5, demonstrates advancements in multitask instruction tuning. 

Detailed guidance on fine-tuning FLAN-T5 for specific applications, such as summarizing customer service chats, illustrates practical use cases. This analysis provides a comprehensive understanding of the strategic considerations involved in choosing between single-task and multitask fine-tuning approaches for LLMs.

Table of Contents:

1. Introduction to Single-Task Fine-Tuning
   1.1. Benefits and Drawbacks of Single-Task Fine-Tuning
   1.2. Catastrophic Forgetting in Fine-Tuning

2. Multitask Fine-Tuning Overview
   2.1. Challenges and Benefits of Multitask Fine-Tuning
   2.2. Introduction to FLAN Models
   2.3. Overview of FLAN-T5
   2.4. Fine-Tuning FLAN-T5 for Specific Use Cases

3. Example: Summarizing Customer Service Chats

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1. Introduction to Single-Task Fine-Tuning

While LLMs have become famous for their ability to perform many different language tasks within a single model, your application may only need to perform a single task. In this case, you can fine-tune a pre-trained model to improve performance on only the task that interests you.

1.1. Benefits and Drawbacks of Single-Task Fine-Tuning

For example, summarization using a dataset of examples for that task. Interestingly, good results can be achieved with relatively few examples. Often just 500–1,000 examples can result in good performance in contrast to the billions of pieces of text that the model saw during pre-training. However, there is a potential downside to fine-tuning on a single task. The process may lead to a phenomenon called catastrophic forgetting.

1.2. Catastrophic Forgetting in Fine-Tuning

Catastrophic forgetting happens because the full fine-tuning process modifies the weights of the original LLM. While this leads to great performance on a single fine-tuning task, it can degrade performance on other tasks.

What options do you have to avoid catastrophic forgetting? First of all, it’s important to decide whether catastrophic forgetting impacts your use case. If all you need is reliable performance on the single task you fine-tuned, it may not be an issue that the model can’t generalize to other tasks. If you do want or need the model to maintain its multitask generalized capabilities, you can perform fine-tuning on multiple tasks at one time.

A second option is to perform parameter efficient fine-tuning, or PEFT for short instead of full fine-tuning. PEFT is a set of techniques that preserves the weights of the original LLM and trains only a small number of task-specific adapter layers and parameters.

2. Multitask Fine-Tuning Overview

Multitask fine-tuning is an extension of single-task fine-tuning, where the training dataset is comprised of example inputs and outputs for multiple tasks. Here, the dataset contains examples that instruct the model to carry out a variety of tasks, including summarization, review rating, code translation, and entity recognition.

2.1. Challenges and Benefits of Multitask Fine-Tuning

One drawback to multitask fine-tuning is that it requires a lot of data. You may need as many as 50–100,000 examples in your training set. However, it can be worthwhile and worth the effort to assemble this data. Let’s take a look at one family of models that have been trained using multitask instruction fine-tuning.

2.2. Introduction to FLAN Models

FLAN models, or Fine-tuned LAnguage Net models, are a family of machine learning models developed by Google Research that focus on improving the performance of language models through instruction tuning. Instruction tuning involves training models on a diverse set of tasks described in natural language, enabling them to follow instructions better and generalize across different types of tasks. Instruct model variance differs based on the datasets and tasks used during fine-tuning. One example is the FLAN family of models.

2.3. Overview of FLAN-T5