Fine-tuning Large Language Models and practical applications
Published on May 6, 2024 --- 0 min read
By Simona Mazzarino

Fine-tuning Large Language Models and practical applications

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Large Language Models (LLMs) have emerged as a groundbreaking technology in natural language processing (NLP), pushing the boundaries of what machines can achieve in understanding and generating human-like text. These models represent a significant boost in AI capabilities, driven by advancements in deep learning architectures and the availability of vast amounts of text data for training, and computational power.

In this blog post, we will delve into the intricacies of LLMs, explore their training process, examine the differences between standard LLMs and fine-tuned variants, discuss various techniques for fine-tuning models and, last but not least, we will describe how we use LLM in Clearbox AI.

What is a LLM?

A Large Language Model (LLM) is a type of artificial intelligence model designed to process and generate human-like text. At their core, LLMs are built on deep learning architectures, with the transformer architecture being one of the most prominent examples. These models are trained on a massive corpus of text data collected from the internet, encompassing a wide range of sources such as websites, books, articles, and more. Through this extensive exposure to linguistic diversity, LLMs develop a nuanced understanding of language patterns, semantics, and context.

LLMs are trained on a multitude of tasks among various domains within Natural Language Processing (NLP). These tasks include but are not limited to:

  • Language Modeling: Predicting the next word or sequence of words in a given context.
  • Text Completion: Generating coherent and contextually relevant text to complete a given prompt or sentence.
  • Question Answering: Understanding questions and providing accurate answers based on available information.
  • Text Classification: Assigning predefined categories or labels to input text based on its content.
  • Text Summarization: Condensing large blocks of text into concise summaries while preserving key information.
  • Sentiment Analysis: Analyzing the sentiment expressed in a piece of text (positive, negative, neutral).

What is a fine-tuned LLM?

The distinction between a standard LLM and a fine-tuned LLM lies in their training objectives and adaptability to specific tasks or domains.

  • A standard LLM, as mentioned before, refers to a pre-trained model that has undergone extensive training on a diverse corpus of text data, resulting in a general understanding of language structure and semantics. These models, such as OpenAI's GPT series, or Meta’s Llama series, are capable of generating coherent and contextually relevant text across a wide range of tasks and domains. However, their performance may vary when applied to specific tasks or domains outside their training data.

  • On the other hand, a fine-tuned LLM is a pre-trained model that has been further trained on a task-specific dataset or domain-specific data. Fine-tuning involves adjusting the parameters of the pre-trained model to optimize its performance for the target task or domain. So, by fine-tuning a pre-trained (standard) LLM, developers can tailor the model's capabilities to better suit the requirements of a particular application, resulting in improved performance and adaptability.

For example, in law firms, fine-tuning a LLM on legal texts, case law databases, and contract templates can enhance its ability to analyze legal documents, identify relevant clauses, and provide legal insights.

Which are the fine-tuning techniques?

Fine-tuning techniques encompass a variety of strategies aimed at optimizing the performance of pre-trained LLMs for specific tasks or domains. Following is a list of some of the key techniques.

Feature extraction (repurposing)

Feature extraction involves treating the pre-trained LLM as a fixed feature extractor. Only the final layers of the model are trained on the task-specific data, while the rest of the model remains frozen. This approach repurposes the rich language features learned by the LLM, offering a cost-effective way to fine-tune the model efficiently.

Parameter efficient fine-tuning

Parameter Efficient Fine-Tuning (PEFT) enhances model performance on downstream tasks while minimizing the number of trainable parameters, thereby improving efficiency and reducing computational costs. This approach selectively updates a subset of model parameters, maintaining comparable performance to full fine-tuning while offering greater flexibility and deployment efficiency.

Full fine-tuning

Full fine-tuning involves training the entire model on the task-specific data, adjusting all model layers during the process. This approach is beneficial when the task-specific dataset is large and significantly different from the pre-training data. While it offers deep adaptation of the model to the specific task, it requires more computational resources and time compared to feature extraction.

Supervised fine-tuning

In supervised fine-tuning, the model is trained on a task-specific labeled dataset, where each input is associated with a correct answer or label. This method guides the model to apply its pre-existing knowledge to the task, enhancing its performance. Common techniques include:

  • Basic hyperparameter tuning: Manually adjusting hyperparameters like learning rate and batch size to optimize model performance.
  • Transfer learning: Utilizing a pre-trained model as a starting point and fine-tuning it on the task-specific data, reducing training time and data requirements.
  • Multi-task learning: Simultaneously training the model on multiple related tasks to improve overall performance.
  • Few-shot learning: Enabling the model to adapt to new tasks with minimal task-specific data, leveraging its pre-existing knowledge.
  • Task-specific fine-tuning: Adapting the model's parameters to the nuances of the targeted task for enhanced performance and relevance.

Reinforcement Learning from Human Feedback (RLHF)

RLHF involves training LLMs through interactions with human feedback, refining the model's capabilities based on real-world feedback. Techniques include:

  • Reward modeling: Generating multiple outputs and optimizing the model to maximize predicted human-provided rewards.
  • Proximal policy optimization: Iteratively updating the model's policy to maximize expected reward while ensuring stability.
  • Comparative ranking: Learning from relative rankings of multiple outputs provided by human evaluators to improve model understanding.
  • Preference learning: Training models to learn from human preferences between outputs, enabling nuanced adjustments.

These fine-tuning methods offer diverse strategies for customizing LLMs to specific tasks or domains, ensuring optimal performance across various applications and use cases.

How do we use LLMs in Clearbox AI?

In Clearbox AI, the integration of LLMs is important for synthesizing textual data within tabular datasets. Specifically, when our clients' datasets, for instance, related to fraud or churn detection, contain textual fields like transaction descriptions or customer comments on services provided requiring synthetic regeneration, LLMs come into play.

To accomplish this, we employ a fine-tuning approach for LLMs, selecting either open-source models (e.g. Llama or Mistral models) or proprietary models (e.g. OpenAI models) based on the sensitivity of the information contained within the original sentences that need to be synthesized. Using the Pattern-Exploiting Training Framework (PEFT), mentioned before, we fine-tune these LLMs for the task of text completion. This process ensures the generated synthetic sentences align closely with the original data's semantic context while preserving privacy and security concerns.

We really care about data privacy, and synthetic data is a valid ally to preserve it. The technique mentioned above is just one of the efforts Clearbox AI does in this direction. For example, we also developed a Python library called NERPII to perform Named Entity Recognition (NER) on structured datasets and synthesize Personal Identifiable Information (PII) to make it similar to the original one, but de-identifiable and private.

Enhancing the landscape of text generation

The adoption of Large Language Models (LLMs) marks a significant advancement in natural language processing, enhancing the landscape of text generation and understanding.

The distinction between standard LLMs and fine-tuned variants lies in their adaptability to specific tasks or domains, with fine-tuning techniques offering a range of strategies to optimize performance.

In practical application, Clearbox AI leverages LLMs to synthesize textual data within tabular datasets, particularly in scenarios like fraud or churn detection, by leveraging the powerful and transformative LLMs in real-world scenarios while preserving privacy.



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Simona Mazzarino is a Data Scientist at Clearbox AI. In this blog, she writes about Natural Language Processing, Text Mining and Text Generation.