The Risks and Challenges of using Proprietary Data for Fine-Tuning

Fine-tuning is a hot topic in the AI landscape. Fine-tuning a large language model refers to the process of adjusting and adapting a previously trained model to better handle certain tasks or specialize in a particular domain.

To be specific, foundational LLMs are initially trained on an extensive collection of varied textual data, giving them a grasp of general language patterns, grammar, and context. This helps the model grasp the intricacies of language and establishes a robust foundation for a general understanding. However, this foundational knowledge doesn't necessarily tailor the model for expertise in specific tasks, it merely equips it with a wide-ranging understanding of language patterns. 

A lot of sectors deploy specialized terminology, industry-specific phrases, and technical lexicons that might not be prominently present in a universal language model's foundational training data. Adapting the model with industry-centric datasets equips it to recognize and produce precise outputs tailored to the company's sectoral context. Through fine-tuning,  LLMs can also be optimized to perform better in a specific task by learning from examples for that domain. 

Smaller LLMs that have been fine-tuned on a specific use case often outperform larger ones that were trained for more generic use cases. For example, Google's Med-PaLM2 is a language model fine-tuned on a curated corpus of medical information and Q&As. Med-PaLM2 is 10x times smaller than GPT-4 but performs better on medical exams.

In a previous article, we presented how fine-tuning can unlock business efficiency and how to use open-source models to adapt them to your specific operations.

This article presents some risks and challenges of using proprietary data for fine-tuning your AI models.

• Up-to-date data: Fine-tuning models on a specific snapshot of a company's data might not account for real-time changes. When chatting with an AI assistant that delivers answers and analyses, users expect the most up-to-date and pertinent information.  Consistently updating the model to deliver this is not only costly but also complex to sustain over time.
• State-of-the-art models: The pace of AI advancement is staggering, with new models emerging frequently. To remain at the forefront, companies may find themselves needing to update their systems multiple times a year in order to constantly improve AI potential performances and efficiency. Indeed, the rapidity of these technological evolutions can leave businesses lagging if they don't adapt themselves constantly.
• Permissions: It's vital to ensure controlled access to company data. You wouldn't want specific employees to retrieve sensitive data by merely posing questions to the model. Simply integrating all available data into an LLM might risk the unintentional disclosure of confidential information.
• Explainability: When using GenAI on data, enterprises expect of course to get accurate answers, but it’s also important that the information provided by the model is verifiable. For example, if a financial analyst receives investment advice from the AI copilot, it is always better to be able to check the reference of the original report or study backing that recommendation. Blindly relying on LLM outputs doesn't allow for such validations. Trust in outputs with references is important, lowering the amount of double-checking needs and time efficiency.
• Forgetting data: The data of a company is typically much less voluminous than the data used to train a standard LLM. Consequently, when fine-tuning such a model, there are chances of either losing a significant portion of its initially acquired general knowledge or not adequately grasping the specifics of the company's unique data.

As such, while fine-tuning/training LLMs is appealing, there are many limitations to this approach. 

In addition, fine-tuning large language models might require specific fine-tuning expertise, and substantial (and costly) GPU memory, with complex and time-consuming setups. 

This is why other AI approaches can be leveraged, in particular a Retrieval Augmented Generation (RAG) system, a system that dynamically and continuously feeds the LLM with updated and relevant information from external data sources. 

In fact, RAG and fine-tuning can work side by side to get better results. Efficient LLM embedding lays the groundwork, ensuring that the model comprehends prompts accurately. Building upon this, fine-tuning adjusts the model to the task or with the comprehensive knowledge at hand, making its responses to prompts more precise and of superior quality.

The harmonious interplay between RAG and fine-tuning can produce remarkable outcomes, empowering AI systems to deliver top-tier responses and complete tasks with higher efficacy.

At Lampi, we provide RAG system that uses retrieved information to generate answers, helping to ensure accurate, up-to-date, and relevant answers. Responses are based on real information retrieved, which ensures high resistance to hallucinations. Any answers provided by Lampi are backed by relevant source, and users can only access information they already have access to. 

When it is relevant, we also provide the expertise to leverage and fine-tune state-of-the-art large language models (LLMs) to provide an AI solution that is always at the forefront of technology in order to maximize your results in your use cases to step ahead of your competitors. 

Our experts are always ready to guide you on your AI journey, helping you understand and navigate the complex world of AI.

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