Fine-Tuning Large Language Models (LLM) for Specialized NLP Tasks

Learn how fine-tuning Large Language Models (LLMs) enhances performance for specific NLP tasks like text generation, translation, and summarization. Discover how customizing pre-trained LLMs with smaller datasets boosts task-specific accuracy while maintaining overall language proficiency.

Fine Tuning Large Language Model (LLM)

Large Language Models (LLMs) have revolutionized natural language processing by excelling in tasks such as text generation, translation, summarization, and question answering. Despite their impressive capabilities, these models may not always be suitable for specific tasks or domains due to compatibility issues. To overcome this, fine-tuning is performed. Fine-tuning allows users to customize pre-trained language models for specialized tasks by refining the model on a limited dataset, enhancing performance in a specific task while retaining its overall language proficiency.

What is Fine Tuning?

Fine-tuning LLMs involves taking a pre-trained model and further training it on a specific dataset. It is a form of transfer learning where a model trained on a large dataset is adapted to work for a specific task. The dataset required for fine-tuning is much smaller compared to the dataset required for pre-training.

Benefits of Fine-Tuning

• Increased performance: The model learns new data, improving output consistency.
• Efficiency: Adapting a pre-trained model avoids the need to train from scratch, which is computationally expensive.
• Domain Adaptation: Fine-tuning helps adapt to domain-specific vocabulary (e.g., medical, law, finance).
• Generalization: The model generalizes better to task-specific patterns.

Why Fine-tune?

The pre-trained or foundation model is trained on a large dataset using self-supervised training. It learns statistical language representation well. Fine-tuning helps steer the model for specific use cases, aligning it with the desired output, reducing the risk of hallucinations, and making the outputs more accurate.

Types of Fine Tuning

Supervised Fine-Tuning

Supervised fine-tuning further trains a pre-trained model on a task-specific dataset with labeled examples (input-output pairs).

input_dataset = "task-specific-dataset";
fine_tune_model(input_dataset);

Task-specific model fine-tuned successfully.

Instruction Fine-Tuning

This type augments input-output examples with instructions, allowing the model to generalize better to new tasks.

PEFT Methods (Parameter Efficient Fine Tuning)

PEFT reduces memory requirements during fine-tuning. Only selected layers or parameters are updated, which makes training more efficient.

Reparameterization (LoRA)

This method uses low-rank matrices to modify the model’s weights during fine-tuning, updating fewer parameters to save memory and compute costs.

dimension = 512;
embedding = 64;
low_rank = 8;
A_matrix = 8 * embedding;
B_matrix = dimension * low_rank;
new_parameters = B_matrix * A_matrix;

New trainable parameters: 4608.

Reinforcement Learning Human Feedback (RLHF)

RLHF aligns a model’s outputs with human preferences. This is typically done after fine-tuning, using reinforcement learning to update model weights based on human feedback.

Prompt Engineering vs RAG vs Fine-Tuning

When to Use Fine-Tuning?

Fine-tuning should be considered when prompt engineering does not yield adequate performance for a specialized task, and when a domain-specific dataset is available.

Fine Tuning Large Language Model Implementation

In this example, we will fine-tune a Flan-T5-base model using the PEFT LoRA method. Flan-T5 is an instruction-tuned version of T5 released by Google. We use the DialogSum dataset, which consists of summarized dialogues.

dataset = load_dataset("knkarthick/dialogsum");
model_name = "google/flan-t5-base";
base_model = AutoModelForSeq2SeqLM.from_pretrained(model_name);
tokenizer = AutoTokenizer.from_pretrained(model_name);

Model loaded successfully.

Training Results

After training the model using PEFT LoRA with a low-rank matrix, the following results were obtained:

TrainOutput(global_step=160, training_loss=3.6751);

Training completed successfully with a loss of 3.6751.