What is LLM? How to Train and Fine-Tune LLMs? (Part 2)

In the previous article, we had a preliminary understanding of LLMs, however, training and fine-tuning are indispensable key steps to maximize the effectiveness of LLMs. This article will delve into the LLM training process, required resources, fine-tuning techniques, and practical application scenarios to help readers understand how to optimize the performance of LLMs.

So, what is Fine-Tuning? This step involves additional training on top of a pre-trained Large Language Model (LLM) using a smaller but more specific dataset to better adapt it to specific tasks or domains. This step and process can be seen as a refinement and adjustment of the pre-trained model’s knowledge, allowing the LLM to be trained more accurately and reduce AI Hallucination.

Core Steps of Training LLMs

The LLM training process is mainly divided into the following stages:

1. Data Collection and Processing

• Data Sources: Involves collecting a large amount of open text data from various sources, such as online resources, books, articles, and databases. The quality and diversity of the dataset are crucial for LLMs to learn comprehensive language patterns and knowledge.
• Data Cleaning: Removing low-quality data to avoid errors or biased content from affecting the model.
• Labeling and Filtering: Ensuring the diversity and quality of data, and labeling according to application scenarios.

2. Data Preprocessing

The collected data is then cleaned, processed, and standardized. This includes tokenization, which is the process of breaking down raw text into smaller units called tokens, which can be words, parts of words, or even individual characters. These tokens are then converted into numerical representations that the model can understand.

3. Pre-training

This is the key stage where the model learns the basic structure and semantics of language from a vast amount of unlabeled data. A key technology used here is self-supervised learning, where the model learns from the data itself without explicit manual labels. For example, the model may be trained to predict masked words in a sentence or the next word in a sequence, which is called causal language modeling. This process allows the LLM to build a broad understanding of grammar, language nuances, and general knowledge. The foundation of many modern LLMs is the Transformer architecture and its self-attention mechanism, which enables them to process input sequences in parallel and focus on relevant contexts. This architecture includes encoders that convert input text into numerical representations and decoders that convert this representation into useful text.

• Using large-scale neural networks (such as the Transformer architecture) to learn language patterns.
• Training the model to understand language structure through self-supervised learning methods, such as Masked Language Model (MLM) or Autoregressive Model.
• Training is time-consuming and requires high computational resources, usually undertaken by enterprises or research institutions.

4. Fine-Tuning

• Supervised Fine-Tuning: Training on a small labeled dataset in a specific domain to adapt the model to professional applications, such as medical, financial, and legal fields.
• Reinforcement Learning from Human Feedback (RLHF): Using human feedback to adjust the model’s behavior and improve the reasonableness and safety of responses.
• Instruction Tuning: Making the LLM better at understanding and executing user instructions, improving the interactive experience.

5. Reinforcement Learning

• Accuracy Evaluation: Measuring model performance using benchmarks (such as GLUE, SQuAD).
• Generalization Ability Test: Ensuring that the model can maintain high performance on different datasets.
• Error Analysis: Identifying possible error patterns for further optimization.

Resources Required for LLM Training and How to Integrate Your Data with the Foundation Model?

Before starting to build generative AI applications, we need to understand how LLMs and other foundation models interact with your data. Training LLMs is a high-cost process involving the following main resources:

• Computational Resources: Usually requires GPUs (such as NVIDIA A100, H100) or TPUs to accelerate training.
• Storage Space: Large datasets and model files usually require hundreds of TB of storage space.
• Time Cost: Pre-training of LLMs may take weeks to months, depending on the model size and computing power.

1. Prompt Engineering

The simplest way to make the model interact with data is to send the data included in instructions or system prompts to the model. This method does not require changing or adjusting the model, is simple and effective, but may have limitations in some use scenarios. For example, static information can be easily added to system prompts to guide model interaction, but frequently changing information (such as sports scores or air ticket prices) is more difficult to handle.

2. Retrieval Augmented Generation (RAG)

RAG technology ensures that the model’s output is based on your data, rather than just relying on the model’s training knowledge. AI applications using the RAG architecture can search data during queries and incorporate relevant information into prompts. This is similar to prompt engineering, but the system can retrieve new context information in each interaction.

RAG methods are suitable for continuously updated data, private data, large-scale and multimodal data, and benefit from the expanding ecosystem, such as simple integration with databases, embedding APIs, and other components.

3. Supervised Fine-Tuning (SFT)

If you want the model to perform specific explicit tasks, you can consider SFT (also known as Parameter-Efficient Fine-Tuning, PEFT). This method is suitable for classification tasks or generating structured output from unstructured text.

Supervised fine-tuning requires providing model input-output pairs. For example, if you want to classify meeting records (such as “Marketing”, “Legal”, “Customer Support”), you need to provide multiple meeting records and their classification labels to let the model learn how to classify correctly.

4. Reinforcement Learning from Human Feedback (RLHF)

If your goals cannot be described with clear categories or are difficult to quantify, such as wanting the model to have a specific tone (such as brand tone or a formal tone of a specific format), RLHF is an applicable method.

RLHF adjusts the model through human feedback. The basic process is as follows: Provide input prompts and two possible output responses, one of which is more in line with your preferences than the other. For example, one response may be correct but too general, while another is both correct and meets your desired language style. The model learns this preference and then generates output that meets the requirements.

5. Distillation

The goal of distillation technology is to achieve two things at the same time:

1. Create a smaller, faster model to improve processing speed.
2. Make the model more suitable for your specific task.

The distillation method allows the larger foundation model to “teach” the smaller model and focus on your data and tasks. For example, suppose you want to have AI help check the tone of all emails to make them more formal, and you want to use a smaller model to complete this task. You can provide the original text and instructions (such as “make this email more formal”) to the large model to get the revised email content. Next, you can use these input-output pairs to train a smaller specialized model to learn how to perform this specific task.

Three Steps to Help You Choose the Right Method?

First, consider the following three questions:

1. Do you need the model to provide references based on your data?
   • If so, use RAG. RAG allows controlling who can access which data sources, helping to reduce hallucinations and improve the interpretability of results.
2. If these requirements are not needed, you need to decide whether prompt engineering is sufficient, or whether you need to adjust the model.
   • If your data volume is small, prompt engineering may be sufficient, especially with the continuous growth of context windows (such as Gemini 1.5’s 1 million Token window), even a large amount of data can be processed through prompt engineering.
3. If you choose fine-tuning, you need to decide the fine-tuning method based on the specificity and quantifiability of the target model behavior.
   • If the model’s output is difficult to describe and requires human intervention, RLHF is a better choice.
   • The choice of other fine-tuning methods depends on personalization needs, budget, and service speed requirements.

This decision tree briefly outlines the logic of choosing a method:

At this point, you might wonder, why not combine multiple methods? For example, you want to fine-tune the model to fit the brand tone and only use your own data to generate answers (RAG).

The answer is yes, and this is usually the best choice! You can fine-tune the model first and then have it perform other tasks. In addition, you can also fine-tune the LLM and then do in-context prompt engineering to ensure the model operates as expected.

In summary, you can flexibly combine the above methods according to your needs to build an AI solution suitable for your business.

With the advancement of technology, fine-tuning LLMs will become more efficient, cost-effective, and able to adapt to more industry needs. If you want to apply LLMs to a specific field, it is recommended to evaluate data quality and computing resources first, and choose the appropriate fine-tuning method to maximize benefits