Artificial intelligence (AI) is being integrated with other technologies that we use in our everyday lives, including our smart watches, our applications and search engines, and as standalone AI tools like OpenAI’s ChatGPT. These Large Language Models (LLMs) are trained with a large amount of text data source from various places - from all the textbooks ever written to the internet, to learn the patterns underpinning human languages.
So how can we start to use AI and LLMs to make us more productive, reduce redundancies, and help us earn more? In this blog, we'll discuss how to create custom LLMs that are specific to our work through a process called model fine-tuning. We'll cover general concepts, practical use-cases, and how to address challenges that come with fine-tuning your own LLMs.
Business Application of LLMs
One of the reasons why this approach is so powerful is that LLMs are starting to achieve human-level performance with certain tasks that we once thought were fundamental to being human, such as writing. With billions of dollars of R&D and model pre-training from both government funding and private institutions, AI applications are being explore for other use-cases, such as drug discovery, logistics, and other industries that certainly need some automations for support.
Despite all the doom-and-gloom about AI taking people's jobs, it is my opinion that AI will certainly eliminate some jobs, but will also augment human productivity and result in new jobs that we can't fathom yet. There is some evidence that this is a possible future - a McKinsey report examined how generative AI would impact revenues across multiple industries, and they predicted that it would increase revenues by 4.8-9.3% (see Figure above), adding $240 billion to $460 billion in the economy. It is clear to me that those who leverage AI in their businesses will feel the productivity enhancements, resulting in a bigger bottom line.
How can you use LLMs for your business?
Say you are a social media manager, and you want to automate some parts of your job to help you do things like copywriting, brainstorm content strategies, and leverage the analytics you're tracking. AI and LLMs can be used to do these things.
To begin, you could use a foundation model as the starting point, which is an AI model that is already trained on a lot of data, like ChatGPT. You would feed it your data, and could see how it does if you just started prompting it. But you would find that the responses from ChatGPT tend to be generic, boring, and templated.
To improve the quality of the outputs, you would then want to train the model further. For example, you can give the model some examples of your previous blog, product descriptions, marketing materials, and customer reviews. You would then train the AI to generate new blog articles, enable it to rewrite existing posts to optimize SEO, summarize long passages of text in more readable ways, write in your tone and style, or train it to analyze the sentiment of your audience.
This process of improving a pre-trained model for specific tasks or domain areas is called model fine-tuning, and it’s how we can make AI useful for our specific use-case or to start new businesses.
What are Large Language Models (LLMs)?
According to IBM, we generate 2.5 quintillion bytes of data everyday. And LLMs can use this vast dataset and examines the patterns and correlations within text data to "learn" how we speak. These pre-trained LLMs don’t just memorize or hard-code grammatical sentence structures and vocabulary.
As described in original GPT paper, LLMs are trained on so much text, they recognize that there’s a certain probability of words that are associated together. For instance, we recognize that the phrase “pet” is likely associated with “food”, “veterinarian”, and “litter”, compared to "law degree", "401Ks", and "microphones".
These AI models create a probabilistic model of language, and with a certain likelihood, they can predict sequence of words, phrases, and sentences that will likely make sense.
Why not just use ChatGPT?
While pre-trained LLMs like ChatGPT are good as starting points, they likely need to be customized for our specific work needs for the following reasons:
- LLMs are pretty dumb when it comes to specific knowledge domains. If we want it to do very specific tasks, such as responding to customers or be used to aid in medical diagnoses, we need to feed it your specific data.
- The internet is a toxic place. Without additional training, LLMs can learn toxic, biased, and false information. Additional fine-tuning is needed to align LLM responses with human preferences and ethics that will not offend your customers.
- LLMs can generate confident sounding answers that are wrong! These are known as hallucinations, and this lead to bad outcomes for your business such as low customer satisfaction, damage to your brand's reputation, and other regulatory actions that result from propagating false information.
Summary
For these reasons, we need to fine-tune LLMs to make them usable for your business. LLM fine-tuning is all about taking this pre-trained LLM that knows the structure of human language, and continue refining this knowledge so that it learns the specific business related to your business.
Torchstack's AI team consists of experienced data scientists, ML engineers, AI researchers, and cloud solution architects that are devoted to creating custom AI solutions. Please reach out to info@torchstack.ai for a free consultation to discuss your specific needs.
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