In the evolving landscape of Generative Artificial Intelligence (AI), two main types of large language models (LLMs) include foundation and fine-tuned models. While foundation models are highly versatile and can be applied to various tasks, fine-tuned models offer expertise for specific applications.
Let’s unpack these models and explore how they drive business innovation.
What Are Foundation Models In AI?
Foundation models are large language models that serve as a base for more targeted or fine-tuned LLMs. They are typically trained on massive datasets from a wide range of sources, and can also be used on their own without further fine-tuning.
The benefit of using such vast quantities of training data is that foundation models perform well at various tasks. For example, they may excel at text summarization, sentiment analysis, question answering, and many other tasks related to natural language processing (NLP).
Moreover, foundation models consist of many learnable parameters that represent the underlying structure of the training data. This means they can handle unfamiliar datasets, allowing them to adapt to tasks they weren’t specifically trained for.
For businesses, this translates to an AI that can quickly adjust to various different tasks, from analyzing market trends or financial reports to automating interactions with customers.
However, a lack of domain-specific information limits the performance of foundation models at certain tasks. For example, most foundation models can’t create personalized, on-brand marketing campaigns (at least not without a lot of trial and error). Doing so would require the model to be trained on:
- Sufficient customer data
- Brand voice and guidelines
- Domain-specific knowledge base
- And more, depending on a company’s requirements.
However, it’s worth mentioning that the quality of training data and the size of the foundation model will significantly impact the model's performance. When foundation models are trained on higher-quality datasets, such as research papers, they perform better than those trained on lower-quality data.
So, even if they’re not trained on brand-specific documents, foundation models trained on vast and high-quality data can still give quite satisfying results.
Examples Of Foundation Models
Let’s explore some of the most notable foundation models: OpenAI’s GPT-3, NVIDIA’s MT-NLG, and Google’s BERT.
OpenAI’s Generative Pre-Trained Transformer 3 (GPT-3) was released in 2020 and has gained widespread popularity. It boasts a count of 175B parameters, along with 499B tokens of training data.
As mentioned, a larger language model will usually perform better than a smaller one.
This graph demonstrates that the larger 175B parameter model can carry out zero-shot and one-shot learning tasks. Zero-shot and one-shot learning tasks measure how well the model can carry out a task with either 0 or 1 labeled examples, respectively.
In other words, they indicate how well the model can adapt to new tasks while only seeing a limited number of examples.
How Can GPT-3 Benefit Businesses?
As a large language model trained on zero-shot and one-shot learning tasks, GPT-3 can relatively easily adapt to new business use cases without seeing relevant previous examples during training.
Some of the scenarios in which this can be useful include:
- Answering customer queries — For example, GPT-3 may be able to accurately interpret and answer a wide range of customer queries, even if it has never encountered such questions during training. This is a significant advancement from earlier models, which would require different rules for different customer interactions.
- Writing marketing content — GPT-3 can, for example, produce blog posts, ads, and social media posts — different types of marketing content — without needing special programming.
- Data analysis - An example of this would be a business trying to understand the initial reception of a product launch. GPT-3 can analyze customer reviews and sales data to quickly provide efficient feedback, even if the model hasn’t been trained on that specific product or market segment.
GPT-3 can also automate time-consuming but high-stakes tasks like document processing. Unlike earlier AI models, it offers businesses a higher degree of accuracy and flexibility, making its outputs more reliable – and the need for human supervision more optional.
Finally, the fact that GPT-3 was trained on such vast data makes it more adaptable and valuable across a range of business tasks. One example is its ability to process various types of documents, from comprehensive market trend analyses to individualized customer feedback surveys.
Cons Of GPT-3 For Businesses
One issue with GPT-3 is that the large datasets used in the training process often lead to “hallucination”. This is where the model creates outputs that aren’t factually accurate by overgeneralizing from the range of scenarios it encountered in the large datasets.
Consequently, GPT-3’s incorrect outputs can have dire financial and strategic consequences for businesses relying on it for decision-making. It can also affect customer trust if a GPT-3 application provides incorrect advice.
Moreover, with GPT-3 being trained on vast amounts of internet data, it’s possible that there was controversial or biased content. The model might produce biased outputs toward certain groups of people. An example of this would be the model being biased towards a specific gender when making recommendations for hiring.
With the release of the highly popular GPT-3 in 2020, NVIDIA also wanted to make its mark in the LLM landscape with one of the largest language models to date: Megatron-Turing Natural Language Generation (MT-NLG).
MT-NLG contains over three times the number of parameters as GPT-3, with 530 billion parameters.
This foundation model performs exceptionally well at various natural language tasks. Examples include:
- Reading comprehension
- Completion prediction
- Language inference
Each of these tasks plays a crucial role in business applications like content generation for marketing, where relevant content significantly impacts brand perception and customer engagement.
What Are the Benefits of MT-NLG for Businesses?
MT-NLG has demonstrated high performance on several natural language processing tasks.
For example, it’s able to perform exceptionally well on tasks like content generation or customer interaction in chatbot applications, which both require a high level of reading comprehension. Reading comprehension helps it better understand source materials, and thus, produce better output.
For businesses, MT-NLG can result in significant cost savings by generating custom, automated responses to customer inquiries with 24/7 availability.
Another benefit is that MT-NLG’s huge scale allows it to generate diverse content across various domains, making it suitable for businesses in different industries.
Cons Of MT-NLG For Businesses
Although MT-NLG’s scale is impressive, it comes with challenges.
Deploying and maintaining such a model may require more computational resources than other models, making it less accessible to businesses with less advanced infrastructure.
Additionally, customization of such a large model can be limited, especially for businesses that want to adjust the model to suit their needs. Although MT-NLG provides a strong base as a foundation model, fine-tuning and customizing a large model like this would require massive quantities of computational resources.
Google released Bidirectional Encoders Representation from Transformers (BERT) in 2018, making it one of the earliest examples of foundation models.
Similar to GPT-3, BERT also uses the transformer architecture. Although BERT has a higher level of contextual understanding than GPT-3, GPT-3 has an advantage when it comes to generating text.
This means BERT is better at tasks such as making recommendations and decision-making by utilizing its high accuracy and contextual understanding. On the other hand, GPT-3 is more suited to applications like content generation for marketing.
What Are the Benefits of BERT for Businesses?
BERT stands out with its ability to better understand the context of a word by reading both the left and right sides of it. Higher precision in language comprehension translates to improved customer interactions and better recommendations overall.
Some of the direct business benefits of BERT include:
- Cost efficiency: BERT’s high accuracy reduces the likelihood of errors. Mistakes such as inaccurate data analysis or flawed recommendations can often be costly, while BERT’s high contextual understanding lowers the error margin.
- Content optimization: BERT’s deep contextual understanding can help businesses understand how their content may be interpreted, which allows them to optimize it before publishing.
- Risk management: Industries such as finance and insurance demand contextual understanding to correctly assess risks. BERT’s high-accuracy reading comprehension can assist with making more informed decisions and help businesses avoid costly mistakes.
Cons Of BERT For Businesses
We've seen how BERT succeeds at challenges involving language comprehension, but it falls short when it comes to text generation. Other models can create cohesive long-form writing, while BERT struggles with this. This means it isn't appropriate for applications like chatbots.
Moreover, BERT has a maximum token limit of 512 for the base model. This means lengthy texts must be separated in order for the model to comprehend them. Dealing with longer reports and documents is challenging with BERT since it will miss out on some context or meaning when splitting the text up.
BERT’s outputs are affected by the order in which information is presented. For businesses looking for consistent results, this can be a concern as it introduces an element of unpredictability into BERT’s outputs.
What Are Fine-Tuned Models?
We’ve explored the broad range of situations in which businesses can use foundation models, However, these models may not be entirely suitable for those with more specialized requirements.
That’s where fine-tuned models come in.
Fine-tuned models are derived from foundation models. They’re trained on a much more narrow and focused dataset in order to make them more suitable for specific applications or use cases.
Rather than being able to carry out a range of broad tasks, a fine-tuned model will specialize in performing one or a few specific tasks.
How Does Model Fine-Tuning Work?
Firstly, AI experts choose a foundation model — a larger language model trained on vast data — that may be the most suitable for a specific downstream task. The foundation model will serve as a basis for later fine-tuning.
The model is then trained on a specific dataset, which results in weight adjustments based on feedback from the new data. This helps the model become more proficient in performing a desired task.
For example, a fine-tuned model for the legal sector may be additionally trained on data focused on corporate law or intellectual property.
Pre-Training vs Fine-Tuning
Both pre-training and fine-tuning are important training processes, but they serve distinct purposes.
Pre-training is a training process for developing foundation models. It uses a large and general dataset, thanks to which the models can successfully capture broad data patterns and excel at various applications.
- Note that pre-trained models (PrLMs) are often used as foundation models, i.e., a starting point for later fine-tuning of a more specialized large language model.
However, pre-training the models on vast amounts of data also has its downsides. One of them, as we saw with GPT-3, is that it may make the models prone to generating inaccurate, biased, or downright false outputs.
This can be especially problematic in industries like healthcare. For example, a pre-trained model may generate incorrect diagnoses or recommendations about a rarer disease if it hasn’t seen many examples of that disease during pre-training.
In addition, the pre-training process raises ethical and legal considerations. One of the biggest ones is data privacy.
The problem is that the broad data used to train the model may contain personal or otherwise sensitive information, and the model’s outputs may reflect or regurgitate it. This is especially true when developers use unvetted Internet data.
Consider a legal AI assistant designed to help with drafting contracts. This model may have been trained on large quantities of publicly available contracts, a few of which contained confidential information. There's a chance that the model will output this information, which would be considered a violation of law in most parts of the world.
Fine-tuned models build upon an already pre-trained (or foundation) model with a much smaller dataset and a subset of machine learning models. Fine-tuning is less computationally demanding than the pre-training process.
Additionally, building upon a pre-trained model means it can be fine-tuned much quicker than training it from scratch. Using a smaller dataset also results in higher efficiency, as the data used for fine-tuning is highly specific to a particular application. This has benefits such as reducing development times and enabling quicker product launches.
Despite both pre-training and fine-tuning having their pros and cons, both processes are often combined to produce powerful yet highly efficient models. By starting with a larger dataset, the model can capture broader knowledge but specialize in a specific area with a smaller dataset afterward.
One concept that combines pre-training and fine-tuning is called transfer learning.
Transfer learning involves using the knowledge from one task to perform another task.
One example would be document classification. A large dataset with many different documents could be used for the pre-training process to teach the model to recognize a wider range of documents (e.g., bills vs. bank statements. This knowledge could then be applied to recognize more specific document types (e.g., paid vs. unpaid bills).
In the above example, the pre-training process would involve first using a larger, more general dataset. The model would then be fine-tuned with a smaller dataset of documents, allowing the model to specialize in a certain use case.
Transfer learning allows the model to achieve high performance with small amounts of labeled data by using the knowledge gained during pre-training as a starting point. This also helps reduce training time and save computational resources.
By applying the knowledge obtained from one domain to another, the model can quickly adjust to the intricacies of a more specific or different task.
Examples Of Fine-Tuned Models
While foundation models provide broad capabilities, it’s their fine-tuned counterparts that bring expertise to industry-specific challenges like document processing, content summarization, and high-accuracy translation.
The gpt-3.5-turbo-8305 model was fine-tuned from text-davinci-002 using Reinforcement Learning with Human Feedback (RLHF). This method helps it achieve better accuracy and produce more ethical (i.e., less biased) outputs.
Businesses can further fine-tune the GPT-3.5 turbo model to improve its performance at a specific task. For example, fine-tuning GPT-3.5 turbo for customer interactions can help ensure the model’s response aligns with the organization’s tone and style, leading to better customer experiences.
Another application where fine-tuning the GPT-3.5 turbo model would be useful is generating or enhancing product descriptions for e-commerce businesses. By fine-tuning the model using appropriate training data, the product descriptions can be made more relevant to the target audience. Examples of appropriate training data include existing product descriptions.
Based on GPT-3, Genei was released in 2021 and specializes in content summarization. By being able to summarize any PDF or website, this language model aims to increase research efficiency.
Genei is a generative AI model that uses unsupervised self-learning techniques for its summarization capabilities. It can also perform other language tasks, such as paraphrasing and question answering.
This model specializes in content summarization due to its ability to understand the context of a word when considering all the other words in the document. As a result, Genei generates highly succinct and accurate summaries.
Genei highlights the potential of a fine-tuned model that addresses niche business challenges, such as streamlining research processes for quicker and more informed decision-making.
As the world becomes more globalized, communication also becomes more paramount. When DeepL released its translator in 2017, it became a pioneer in breaking down language barriers.
Compared to the well-known Google Translate, DeepL Translator had higher accuracy in a number of languages. Some examples include Spanish, German, and French.
One key benefit of being able to translate with such high accuracy is that businesses can interact with international partners and suppliers. Any emails or contracts can be translated with high precision while keeping the original message.
Moreover, customer experience is a core focus for almost any business. The DeepL Translator allows businesses to cater to customers in multiple countries and improve customer satisfaction.
Improved translation accuracy also opens doors for international collaborations that would be difficult otherwise. This means innovation becomes more of a possibility by combining diverse expertise and insights.
Using Foundation and Fine-Tuned Models for Your Business
While foundation models offer breadth, fine-tuned models offer depth. Both have their advantages, but most businesses would benefit more from fine-tuned models.
Models trained on business-specific use cases or documents can achieve better accuracy and act as more reliable companions. This allows businesses to use them to automate their workflows without worrying about errors or needing extensive human supervision.
Interested in automating your workflow using fine-tuned large language models? Contact us today to learn more about our Custom AI Agents.
Are foundation models the same as pre-trained models?
While this is often the case, foundation models are not necessarily pre-trained models (PrLMs) in the purest sense of the term. Developers can use both pre-trained models and fine-tuned models as the foundation for further fine-tuning.
- For example, ChatGPT is fine-tuned from OpenAI’s gpt-3.5-turbo-0301, which isn’t a pure pre-trained or base model. gpt-3.5-turbo-0301 itself is a model fine-tuned from text-davinci-002, so we wouldn’t necessarily call it a PrLM.
With that in mind, we can’t say that foundation models are always the same as pre-trained models. However, they could be considered “pre-trained” from a fine-tuning perspective — even fine-tuned models are considered pre-trained when compared to further fine-tuned models derived from them.