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What is LLM Fine-Tuning? – Everything You Need to Know [2023 Guide]

While standard LLMs excel in generalized tasks, real-world applications demand robust and domain-specific models. Thus, practitioners must fine-tune them on domain-specific data to suit downstream real-world tasks. This post explores fine-tuning LLMs, its benefits, challenges, approaches, and the steps involved.

What is LLM Fine-Tuning? – Everything You Need to Know [2023 Guide]

Over the last months, the Artificial Intelligence (AI) industry has been undergoing a major revolution due to the widespread adoption of advanced Large Language Models (LLMs), such as GPT-3, BERT, and LlaMA. These LLMs can generate output indistinguishable from what a human would create and engage with users’ queries in a contextualized setting. Out-of-the-box they can perform some of the tasks previously reserved only for humans, like creative content generation, summarization, translation, code generation, and more.

Due to their versatility, many business domains are currently adopting LLMs as a critical strategic component to streamline workflows and improve productivity. For instance, in e-commerce, companies use LLMs to build virtual assistants that help visitors search for items they want; in healthcare, LLMs help generate and interpret medical reports; and in marketing, LLMs help generate relevant ad content such as ad copy and creatives.

Though standard LLMs can perform well on generalized tasks, real-world applications require much more robust and domain-specific models. Therefore, practitioners must fine-tune them on domain-specific data to make them suitable for downstream real-world tasks. This post discusses the concept of fine-tuning LLMs, its benefits and challenges, approaches, and the steps involved in the fine-tuning process.

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What is LLM Fine-Tuning?

A Large Language Model, like GPT-3 or BERT, is a general-purpose tool trained on an extensive text corpus consisting of various text sequences from diverse sources. Using it directly to perform a specific job, such as generating a summary of a medical document, can lead to sub-optimal outputs.

LLM fine-tuning is essentially adapting a pre-trained LLM to suit a particular task or application by further training it on a domain-specific dataset. This adjusts the LLM's parameters to suit the new domain-specific data and improves the model performance for a better user experience.

Common misconceptions of LLM fine-tuning

While the concept of LLM fine-tuning is straightforward, a few misconceptions make fine-tuning challenging to understand. Let’s go over them one by one.

Fine-tuning vs. Retrieval-augmented Generation (RAG)

Confusing RAG with fine-tuning is a common mistake, as the two processes seem similar. In a nutshell, the RAG process consists of three key parts:

  1. Retrieval: Based on the user query (prompt), retrieve relevant knowledge from a knowledge base.

  2. Augmentation: Combine the retrieved information with the initial prompt.

  3. Generate: pass the augmented prompt to a large language model, generating the final output.

The process does not change the underlying model, though. Only the user input is impacted.

Fine-tuning vs. Few-shot learning

Another point of confusion relates to the differences between fine-tuning and few-shot learning. Few-shot learning is a learning paradigm that enables models to adapt to new tasks or data with minimal additional examples. For instance, a model trained to classify various objects might be adapted to recognize specific types of plants or animals using just a few images of each new category. This is distinct from fine-tuning, where the pre-trained language model is retrained on a new, often larger dataset to specialize in a particular task.

Fine-tuning vs. continual learning

Continual learning is a form of multi-task learning where a model learns new information sequentially to perform new tasks without forgetting previous concepts. Fine-tuning is part of continual learning, as the latter requires models to adapt to a particular task incrementally. The result of continual learning is a model that works well in multiple domains instead of a single one.

Data requirements for fine-tuning

A common misconception is that fine-tuning large language models requires large training data. In reality, the extensive pre-training that these models went through when they were being built, based on datasets that consisted of diverse and large knowledge corpora, means they have already acquired a vast foundation of knowledge. When they’re being fine-tuned for specific tasks, this pre-existing knowledge enables them to adapt effectively to much smaller, targeted datasets. Therefore, proper data curation and quality, rather than volume alone, are often the keys to fine-tuning for a better-performing LLM.

For instance, in their Less is More for Alignment (LIMA) paper, Meta’s researchers describe fine-tuning a pre-trained LLM using only 1,000 text sequences consisting of questions and answers from community forums, such as Stack Exchange and wikiHow, plus a few other prompts generated manually. The fine-tuned model performed better than state-of-the-art models like OpenAI's DaVinci 003 and Alpaca, which was fine-tuned using 52,000 examples.

What are the benefits of fine-tuning an LLM?

Fine-tuning language models offer many advantages, allowing experts to streamline the development of the machine-learning model. Below are just some of the benefits of tuning large language models:

Task-specific adaptation

As discussed, a large language model has exceptional natural language understanding thanks to extensive training datasets from various sources. Users can exploit this knowledge by fine-tuning the model for a specific downstream task, such as text classification, sentiment analysis, machine translation, question-answering, etc., for specific domains like finance, healthcare, and cybersecurity.

Domain-specific expertise

Fine-tuning large language models lets users tailor a foundation model to a particular domain for improved performance. For instance, healthcare professionals can leverage a fine-tuned model for medical diagnosis, to recognize diseases accurately based on symptoms.

User experience enhancement

AI practitioners can integrate fine-tuned models with several applications to boost user experience. For instance, retailers can fine-tune an LLM to build a chatbot that provides relevant recommendations to visitors to help them with purchasing decisions, based on their requirements.

Computational efficiency vs. training from scratch

Fine-tuning an LLM doesn’t require as much computational power than training the model from scratch. Since LLMs already have extensive knowledge, users can quickly adapt them to suit their requirements, leading to a more efficient training process and a faster time to market.

LLM fine-tuning approaches

Developers can use several fine-tuning techniques for efficient LLM adaptation. Standard methods include supervised fine-tuning, reinforcement learning, and self-supervised fine-tuning. Let's talk about each one of them in more detail.

Supervised fine-tuning

In supervised fine-tuning, developers can adapt a pre-trained model using labeled data. This technique involves adjusting a model's parameters by updating gradients based on ground-truth labels, which helps the model learn patterns in the underlying data distribution. Practitioners use the following approaches to supervised learning:

Transfer Learning

Transfer learning is a machine learning method where a model developed for one task is later reused as the starting point for another task.

This technique mainly involves freezing the first few layers of a deep-learning model that contain information regarding low-level features and then updating model parameters in the subsequent layers using the new dataset.

Task-specific fine-tuning

Task-specific fine-tuning occurs when AI practitioners use a labeled dataset to train a model for different tasks. In NLP, this means language tasks such as question-answering, entity recognition, parts-of-speech tagging, etc. Task-specific fine-tuning aims to optimize a model for a single job, for exceptional performance.

Domain-specific fine-tuning

Using domain-specific fine-tuning, AI practitioners can train an LLM for a particular industrial task. For example, healthcare professionals can fine-tune an LLM to summarize the main points from the medical literature related to a specific disease.

The procedure involves using domain-specific data, such as medical research papers and reports, to help the model learn context-level information and understand the relevant terms for optimal performance.

Instruction fine-tuning

Instruction fine-tuning takes the concept of fine-tuning a step further by incorporating specific prompts or directives into the training process. This method guides the language model's behavior more directly, offering developers enhanced control over how the model responds to certain types of input.

While instruction fine-tuning might seem similar to RAG in that both techniques aim to refine the model's responses, there are key differences. Instruction fine-tuning embeds specific behaviors within the model, while RAG retrieves external information to enhance responses. Instruction fine-tuning ensures consistent, controlled outcomes based on provided guidelines, beneficial where adherence to styles or consistency matters. Combining both can be powerful for advanced tasks, leveraging RAG's external information and fine-tuning's adherence to guidelines. In sectors like finance, law, or healthcare, this combination ensures accurate, industry-specific responses.

Reinforced Learning from Human Feedback (RLHF)

RLHF is an evolving fine-tuning technique that uses human feedback to ensure that a model produces the desired output. The process includes domain experts who monitor a model's output and provide feedback to help the model learn their preferences and generate a more suitable response.

In particular, the process augments the supervised fine-tuning procedure by training a reward model that automatically optimizes the primary model's performance. The training data for the reward model can contain human preferences, allowing it to understand the most preferred output, based on subjective rankings.

Once trained, the reward model can assess the primary LLM's output and generate feedback using a reward score. The feedback will allow the LLM to produce a suitable response by leveraging the reward score.

Self-supervised fine-tuning

While supervised fine-tuning and RLHF require labeled data, self-supervised methods require only part of an input sequence to predict the remainder and use it as ground truth to train a model. The practice helps overcome the challenge of limited or no labeled data within specific domains.

The following explains three primary self-supervised fine-tuning techniques: Masked Language modeling, contrastive learning, and in-context learning.

Masked Language Modeling (MLM)

MLM involves masking or hiding a portion of an input text sequence and letting the model predict the appropriate missing tokens. Popular models that use the MLM technique include BERT, RoBERTa, and ALBERT.

The models use the transformer architecture, to understand the tokens appearing within a masked input sequence. For instance, an MLM can predict the hidden bit in "the quick brown...jumped over the lazy...".

Contrastive learning

Contrastive learning is a method in machine learning where the model learns to understand a dataset without needing labels. It does this by figuring out which pieces of data are similar or different from each other. For example, think about how you can tell cats and dogs apart: cats have pointy ears and flat faces. In contrast, dogs might have droopy ears and a more pronounced nose.

Contrastive learning lets a machine learning model do something similar: it compares different data points to learn important characteristics about the data, even before it's given a specific task like sorting or categorizing.

In-context learning

With in-context learning, an LLM learns to predict responses using context-level information provided through specific prompts or additional textual descriptions.

For example, when provided with a prompt and a few examples of classifications, the LLM uses these examples to guide its response to a new classification task.

Challenges of fine-tuning LLMs

While the fine-tuning process itself can be straightforward, meeting the requirements to develop an effective fine-tuned model is challenging. Below are the most common challenges that practitioners face when fine-tuning LLMs.

Data quality

Fine-tuning is as effective as the quality of training data, especially in supervised fine-tuning processes where data labeling quality plays a vital role. Ensuring data quality implies checking the data for inconsistencies, missing values, bias, outliers, formatting issues, etc.

In addition, due to data scarcity in specific domains, maintaining data representativeness becomes challenging, since only a few samples are available for training. As such, robust data curation becomes necessary to streamline the data collection and transformation process for effective fine-tuning.

To tackle these problems, consider using an automated tool like the Kili app. Kili helps project admins orchestrate data workflows that facilitate creating error-free datasets, with automated error-spotting and correction, constant monitoring of dataset quality through the use pre-defined metrics, and many more. To learn more about Kili’s quality features, check out our documentation.

Computational resources

While not as resource-hungry as training a model from scratch, fine-tuning can still be time-consuming and costly for smaller AI teams that do not have the required infrastructure. For example, fine-tuning an LLM like GPT-3 and BERT to the point when you get a decently performing model may require updates to thousands of model’s parameters.

What’s more, fine-tuning techniques that require properly labeled data often require hiring domain-level experts to assess annotation performance and guide the process. The additional cost of hiring such experts may put extra strain on a company's restricted budget and make fine-tuning a risky feat.

Catastrophic forgetting

Catastrophic forgetting mainly occurs in continual learning environments where a model gradually forgets previous knowledge when learning data patterns for a new task. Essentially, model's weights change significantly during the learning process, causing it to perform poorly on older tasks.

Techniques to mitigate catastrophic forgetting include elastic weight consolidation (EWC), which prevents weights that are important for specific tasks from changing significantly; Progressive Neural Networks (ProgNet), which adds a network for each new task; and Optimized Fixed Expansion Layers (OFELs) which adds a new layer for each task.

Evaluation metrics

Evaluating an LLM for domain-specific needs can be challenging: currently, no standardized evaluation frameworks exist. What’s more, in assessing the abilities of LLMs, one sometimes needs to take into account additional industry-specific factors. For example, in critical industries such as healthcare, an LLM-powered application must be trusted not to recommend incorrect diagnoses or treatment. This adds an extra layer of complexity to the evaluation process.

A platform like Kili helps AI practitioners by providing a consolidated approach to evaluation. We recently held a webinar going through the challenges of LLM evaluation, and Kili’s platform solves some of these challenges by combining human evaluation and model automation.

Step-by-step process of fine-tuning an LLM for domain-specific needs

You can mitigate all the above-mentioned challenges by following a standard fine-tuning process and ensure efficient model development for domain-specific needs and downstream tasks.

The steps below demonstrate a practical framework for fine-tuning a Large Language Model to build a chatbot application.

Step 1 - Prepare your data

You can begin by collecting relevant task-specific data for the chatbot, which, in this case, would be a series of questions and answers, open-ended conversations containing different prompts and responses, and multilingual text sequences (if you want your chatbot to understand other languages.

Step 2 - Choose an LLM

The next step is to select a pre-trained LLM for fine-tuning. The selection criteria can include assessing the LLM against existing benchmarks and performance indicators for finding the appropriate option.

Step 3 - Perform fine-tuning

Then comes the fine-tuning phase, where you adapt the LLM to your needs using the pre-processed task-specific data. Transfer learning with RLHF is a viable strategy to fine-tune the model for a chatbot.

Step 4 - Conduct robust evaluation

After fine-tuning, you can validate model performance through appropriate metrics. For an LLM-based chatbot, the following metrics, when combined, can help measure its performance:

  • METEOR (Metric for Evaluation of Translation with Explicit Ordering):

    Imagine METEOR as a smarter way to check if a translated piece of text is good. Instead of just looking for exact word matches, it also considers synonyms and paraphrasing. This makes it a better tool for evaluating translations or written content where the way things are said can vary but still be correct.

    This is particularly relevant in chatbot conversations, where there are often many correct ways to respond to a user's query.

  • Perplexity:

    Perplexity measures how well a language model predicts a sample of text. In the context of a chatbot, it can be used to evaluate how well the language model predicts the sequence of words in its responses. A lower perplexity score indicates that the chatbot's responses are more predictable and fluent based on its language model. This can signify linguistic coherence and fluency in the generated responses.

  • Diversity metrics (Distinct-N):

    Diversity Metrics, such as Distinct-N (Distinct-1, Distinct-2, etc.), can be quite useful in evaluating certain aspects of a chatbot, particularly in terms of the variety and richness of its responses. They count how many different single words (unigrams) and two-word combinations (bigrams) are used. More variety usually means more interesting and less repetitive text.

    These metrics provide insights into how varied and unique the chatbot's language use is, which is an important aspect of creating engaging and natural-sounding conversations.

  • Human evaluation:

    Human judges assess aspects like coherence, relevance, context understanding, empathy, and conversational flow, which automated metrics cannot fully capture. Typically, evaluators are given criteria to assess an LLM’s output and then use a tool to rank, rate, categorize, or label the output, depending on what the LLM is being evaluated on.

    To evaluate the quality of an LLM output, evaluators can use different techniques such as the Likert scale (ranging from 1 to 5) to rate its relevance, fluency, or informativeness. In addition, to assess the accuracy of an LLM, evaluators can employ data labeling techniques to identify incorrect statements and categorize them into specific types of errors such as factual inaccuracies, topic deviations, non-sensical responses, and so on.

Each of these metrics provides a different lens through which you can assess an LLM. The choice of metrics should align with the specific goals and context of your application. Often, a combination of several of these metrics and qualitative assessments offers the most comprehensive evaluation of an LLM's performance.

Step 5 - Test deployment

Deploy the model in a test environment to detect anomalies and identify issues. The process will help you fix errors early and avoid production incidents.

Step 6 - Iteration

The testing phase can also include feedback collection from a few users, domain experts, and other automated systems. You can use the insights from such feedback to improve the chatbot's output by fine-tuning it using a more relevant dataset.

Step 7 - Production deployment

Finally, you can integrate the model with your chatbot application. It's advisable to build continuous monitoring and observability workflows to resolve issues in the real world quickly.

Leverage an all-in-one fine-tuning platform

Fine-tuning LLMs offers significant benefits by adapting general pre-trained models to specific tasks. Fine-tuning techniques help developers leverage LLMs' vast knowledge to build models that can improve productivity in multiple business domains.

However, the process relies heavily on data quality to ensure that fine-tuning produces the desired results. And since maintaining quality is challenging, platforms like Kili offer various features to build high-quality datasets through robust QA and collaboration workflows. Kili offers a range of tools that can assist in the fine-tuning of GPT-type models, providing support throughout the process. For successful LLM fine-tuning, it enables practitioners to establish custom evaluation criteria, set up high-quality data labeling workflows, identify significant feedback, integrate with leading LLMs, and access qualified data labelers.

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