Fine Tuning ChatGPT is a Waste of Your Time

Summary

The video script discusses the complexities and potential pitfalls of fine-tuning AI models, suggesting it might be overrated. It highlights the context window problem, where AI struggles with large contexts, and the challenge of defining training data to avoid overtraining. The script introduces Retrieval Augmented Generation (RAG) as a more flexible alternative to fine-tuning, allowing for easier updates and better data control. It concludes by emphasizing the vast potential of RAG and its applications in creating more autonomous AI systems.

Takeaways

• 🔧 Fine-tuning is a technique for customizing AI models but is complex and data-intensive.
• 📈 Major AI companies like OpenAI, AWS, and Microsoft are investing in fine-tuning capabilities.
• 📚 Fine-tuning is popular due to its potential to make AI models more specialized and personalized.
• 🧠 AI models have limitations, particularly with context memory, which fine-tuning aims to address.
• 🔍 Defining the right data for fine-tuning is challenging, as it requires understanding the model's current knowledge gaps.
• 🚧 Overtraining is a risk with fine-tuning, where the model becomes too specialized and loses general applicability.
• 🌐 OpenAI's approach to training uses a diverse corpus from the internet to create more believable responses.
• 🔄 Fine-tuning is static; once trained, the model's knowledge is fixed until retrained.
• 🔑 Retrieval Augmented Generation (RAG) offers a more flexible alternative to fine-tuning by using searchable data chunks.
• 🔄 RAG allows for updating and managing data chunks easily, providing a dynamic approach to AI model enhancement.
• 🛡️ Security is a concern with AI systems, as they can expose sensitive data through user interactions.
• 🚀 RAG opens up possibilities for more advanced AI applications, such as autonomous agents with complex decision-making abilities.
• 📢 The speaker encourages following their platform for more discussions on AI, indicating the ongoing relevance of these topics.

Q & A

What is fine-tuning in the context of AI models?

Fine-tuning is a technique where an AI model is further trained on a specific dataset to adapt to a particular task or to better suit a user's needs, making it more personalized.

Why is fine-tuning considered complex and data-intensive?

Fine-tuning is complex and data-intensive because it requires a significant amount of relevant data to effectively retrain the model to perform well on a specific task, and it involves understanding the nuances of the data to avoid issues like overfitting.

How has OpenAI made fine-tuning more accessible?

OpenAI has made fine-tuning more affordable and provided a series of guides to help users fine-tune their models, making it easier for them to adapt the latest AI models to their needs.

What is the context window problem in AI?

The context window problem refers to the limitation in the amount of contextual information an AI model can process and remember when generating responses, which can lead to loss of context and understanding in longer conversations or responses.

Why is defining the data for fine-tuning challenging?

Defining the data for fine-tuning is challenging because it requires identifying what the model does not know and how to provide it with the necessary information without overtraining or causing the model to become too narrowly focused.

What is overtraining in the context of AI models?

Overtraining occurs when an AI model is trained too much on a specific set of data, leading it to perform well on that data but poorly on new, unseen data, as it fails to generalize well.

How does Retrieval Augmented Generation (RAG) differ from fine-tuning?

RAG differs from fine-tuning by using smaller chunks of data that can fit within the model's memory space, allowing for more flexible and updatable responses. It involves searching for relevant data chunks to answer questions rather than relying on a pre-trained model's knowledge.

What are the benefits of using Retrieval Augmented Generation over fine-tuning?

RAG allows for more updatable and flexible responses as it breaks down data into smaller, manageable chunks. It also provides better control over which documents are sent to users, enhancing security and the ability to customize the AI's knowledge base.

How does the security of data differ between fine-tuning and Retrieval Augmented Generation?

With RAG, there is a stronger ability to control which documents are sent to specific users, allowing for better data security and customization of the AI's knowledge based on user needs. Fine-tuning, on the other hand, locks the model's knowledge in time, making it less adaptable.

What are some potential applications of Retrieval Augmented Generation?

RAG can be used to develop autonomous agents with the ability to perceive, plan, and act based on stored details about their environment, simulating a more human-like decision-making process in various applications.

How can one stay updated with more insights on AI like the ones discussed in the script?

One can follow the Disabled Discussion podcast and publication for regular updates and discussions on various AI topics, providing further insights and exploration of AI capabilities.

Outlines

00:00

🤖 The Illusion of Fine-Tuning in AI

This paragraph delves into the complexities and misconceptions surrounding AI fine-tuning. It highlights the process as being data-intensive and currently en vogue within the AI community. Major companies like OpenAI, AWS, and Microsoft are investing in fine-tuning capabilities, but the speaker questions its necessity, pointing out the limitations of AI models in context retention and the difficulty in defining training data. The paragraph also touches on the risks of overtraining and the challenges of keeping a fine-tuned model updated with new information, suggesting that the approach may not be as effective as it sounds.

05:03

🔍 Exploring RAG as an Alternative to Fine-Tuning

The second paragraph introduces Retrieval Augmented Generation (RAG) as a more flexible and potentially superior alternative to fine-tuning. RAG involves breaking down data into manageable chunks that can be easily searched and referenced by AI models, addressing the context window problem. The speaker discusses the benefits of RAG, such as the ability to update data chunks in real-time and the enhanced control over the information provided to users. Additionally, the paragraph touches on the security implications of using AI systems and the innovative applications of RAG, such as creating autonomous agents with advanced cognitive patterns. The speaker concludes by emphasizing the vast potential of RAG compared to the more limited fine-tuning approach.

Keywords

💡Fine Tuning

Fine tuning refers to the process of adapting a pre-trained AI model to a specific task or domain by training it on a smaller, more focused dataset. In the context of the video, it's portrayed as a complex and data-intensive technique that many AI enthusiasts and companies are currently pursuing. However, the script suggests that while it's popular, it may not always be the most effective approach due to challenges like overtraining and the context window problem.

💡AI Model

An AI model, in this video, is a machine learning system that has been trained to perform specific tasks, such as understanding and generating human-like text. The script discusses how these models can be made 'your own' through fine tuning, but also highlights the limitations they have, such as the context window problem, which affects their ability to understand and generate long, coherent responses.

💡Context Window Problem

The context window problem is a limitation in AI models where they struggle to maintain and utilize long-term context in their responses. The video script uses this term to describe the issue of AI models losing parts of the context when generating long answers, which is a significant challenge when trying to fine tune these models to understand and respond to complex queries that require extensive context.

💡Overtraining

Overtraining is a concept in machine learning where a model is trained excessively on a particular dataset to the point where it starts to perform poorly on new, unseen data. The script uses the analogy of driving the same route home every day to explain overtraining, suggesting that if an AI model is fine-tuned in a very specific way, it may not be able to adapt to new or different situations effectively.

💡Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation is an alternative approach to fine tuning where relevant data is broken into smaller chunks and then searched for appropriate pieces to answer a given question. The script highlights RAG as a more flexible and updatable method compared to fine tuning, allowing for the inclusion of more recent and varied information in the AI's responses.

💡Large Language Models (LLMs)

Large Language Models, or LLMs, are AI models that have been trained on vast amounts of text data and are capable of generating human-like language. The video script discusses the challenges of working with LLMs, such as the context window problem, and how techniques like RAG can be used to improve their performance and flexibility.

💡Data Intensive

The term 'data intensive' describes processes or systems that require a large amount of data to function effectively. In the video, fine tuning is described as a data intensive process, implying that it requires significant resources and datasets to train AI models to perform well on specific tasks.

💡Representative Data

Representative data refers to a dataset that accurately reflects the variety and characteristics of the real-world scenarios the AI model is intended to understand or interact with. The script emphasizes the importance of having representative data when fine tuning AI models to ensure they can respond appropriately to a wide range of situations.

💡Corpus

A corpus is a large, structured set of texts that is used to train or test AI models. The video script mentions that companies like OpenAI use a corpus of information from various sources, such as forums, articles, and news, to train their models to generate believable responses.

💡Security

In the context of the video, security refers to the protection of data and the control over the information that AI models are exposed to. The script discusses the potential risks of users extracting sensitive information from AI systems and the importance of controlling which documents are sent to which users to maintain data privacy and security.

💡Autonomous Agents

Autonomous agents, as mentioned in the script, are self-directed systems that can perceive, plan, and act in their environment. The video discusses a paper where retrieval augmented generation is used to develop autonomous agents that can live and interact in a simulated environment, showcasing the potential for advanced AI applications beyond simple data processing.

Highlights

Fine tuning is a technique for customizing AI models but is often misunderstood and overhyped.

Major AI companies like OpenAI, AWS, and Microsoft are investing in fine tuning capabilities.

Fine tuning is complex and data-intensive, requiring a deep understanding of the AI model's limitations.

The context window problem limits the AI's ability to understand and retain information over extended conversations.

Defining the right data for fine tuning is challenging due to the need to identify gaps in the model's knowledge.

Overtraining is a significant risk in fine tuning, where the model becomes too specialized and loses generalizability.

OpenAI's approach to fine tuning involves using a diverse corpus of information from various sources.

Fine tuning can lead to models that are too rigid and unable to adapt to new or changing information.

Retrieval Augmented Generation (RAG) is presented as a more flexible alternative to fine tuning.

RAG allows for breaking down data into manageable chunks that fit within the AI's memory constraints.

Updating documents in RAG is more straightforward compared to the static nature of fine-tuned models.

RAG provides better control over the information provided to users, enhancing security and customization.

The potential applications of RAG are vast, including the development of autonomous agents with complex behaviors.

RAG offers more scalability and adaptability compared to the limitations of fine tuning.

The speaker emphasizes the importance of understanding and securing the data used in AI models.

The future of AI development may lean more towards methods like RAG rather than traditional fine tuning.

The speaker invites the audience to follow for more discussions on AI, hinting at ongoing and future content.

Transcripts

Today we're going to talk about fine tuning and why it's

probably a waste of your time.

Fine tuning is a technique for taking an AI model and really making it your own.

It's a very complex and data intensive process and it's all the rage right now.

Everyone who starts to get into AI thinks that fine tuning is this kind of end all

be all but the reality is far from that.

Many of the major AI companies are focused on fine tuning.

OpenAI has recently made fine tuning significantly more affordable.

They have an excellent series of guides about how to use some of their latest and

greatest models, and fine tune them in a way that helps make them part of your own.

AWS just a few days ago, discussed a stack that they're developing with their

infrastructure to help support developers and they're planning this sort of,

Entire model with the understanding that they are going to need to develop this

infrastructure built around training.

AWS, Microsoft, OpenAI, all these companies are not alone in thinking

that this is the way forward.

Many AI enthusiasts are also hearkening the call to fine tune models.

And fine tuning itself even sounds like something that you need to do.

Well, why do we want to fine tune a model?

The reality is it's because of how we interact with these AI and really

the limitations that they have.

When we take a general AI model such as OpenAI or even Llama2,

and we ask it a question.

It has an available sort of memory space for us to ask that question in, right?

We also need memory space for it to be able to process that

question and return us an answer.

If that answer kind of goes beyond that space, or it needs to generate such

a long answer it'll eventually begin to lose part of the context as well.

And then we have all of the sort of related information that we really

want to provide for it to be able to understand what we asked, if I'm

having a conversation with an AI and I reference something I just said,

right, in the chat just a few messages ago that information is relevant.

Unfortunately, we've got a lot of relevant information that we really

want to give the AI and we really don't have a place to put it.

And this is the context window problem.

When you hear about large language models struggling with their larger

contexts, that's actually what this is.

It's this problem where we can't actually pass enough information in.

The challenge really with fine tuning though, is that it's difficult for us

to understand how to define the data.

We need to know what the model that we have today doesn't know and

how to tell it more information.

I could be referencing documentation that it doesn't actually know about.

I could be referencing information from my company that I needed to understand.

Or, I actually could be having it help me generate a report, and I actually

need it to be able to understand the structure of that report.

So, you know, we try and define some training data, or give it some

examples of, of how it should respond.

The difficulty here is really there's some pitfalls when you go through

One of those major challenges is the challenge of overtraining.

And when we think of overtraining, we think of we can think of it

kind of like you're driving a car

if the normal way that you get home from work is every day you kind

of go and you're driving down the road and you always turn off, you

know, down toward wherever your, you know, your home is, right?

You kind of get in the habit of doing this day after day, you kind of are

used to at around this time of day, we usually do the right thing that we were

planning to do and we go home, right?

If we kind of structure an AI model around the same way, right?

We say, okay, given this problem, the outcome is usually to go home, right?

The difficulty is that there might be other things that change.

There might be something, like, really valuable that we actually

want to get done at that time of day instead of going home, right?

Might have some other task we'd prefer it to do.

Or there might actually be a challenge with the task that we normally do.

There may be a hazard or something else that the AI needs to watch out for.

If we over train that this is always the correct path home,

It's not going to be able to respond to these other stimuli as effectively.

So when we think about training data and giving this training data to our

model in order to be able to fine tune it, we have to think about what is a

representative set of data that would represent the world around the problem

that we're trying to solve, right?

The way OpenAI has kind of solved this is it took in a huge corpus of information

around the entire internet, right?

They took sources from forums, they took sources from articles, they took

news sources and published documents.

They have a corpus of information that is from many different facets, and that's

what makes such a believable response.

When we use a fine tuned training set that's based on some set of

answers that we know, we're somewhat limited to that set of known answers.

Unless we can give it a more representative understanding of

what the real world looks like.

We get into this problem where it goes toward the goal that we've told it exists,

and it's blind to any other solution.

So a path with a lot less difficulty is RAG, which is

Retrieval Augmented Generation.

Effectively, we're taking that same set of related data from before

and breaking it up into pieces.

Now, these smaller pieces can easily fit within the available space, right?

They can fit in with the answer and the question, no problem at all.

And what we can do is we can search for chunks that are appropriate for

answering the question that we have.

If we're asking a question about a certain character from a book, we could look up

sources in that book or paragraphs in that book that relate to that character.

Which gives us a much better probability of answering that question.

The training with Retrieval Augmented Generation is really taking your

data and making it available for this, these sorts of tools, right?

By passing in these small chunks and then sending just this set of information to

an LLM or an AI we're able to more easily manage the difficulties of this problem.

Another big benefit of retrieval augmented generation is that when we develop

these chunks, we can always update these chunks of documents as we want.

When we're talking about something like fine tuning, you really

need to do that ahead of time.

You're not able to quickly change and update the data that the AI knows.

It's locked in time, similar to OpenAI.

They release these new models.

Those models only know a bit of history up until a certain point, and

after that, they don't know anything.

Your fine tuned model will be the same.

As you change data, you will need to then go back and retune it to

be able to handle that situation.

Security is another issue.

With any of these systems, users can ask questions to pull out data from

your related information, and even data about the way that you are asking the

system questions and how it is answering.

While these methods of how you use an LLM are going to change

a lot as new models come out.

And as you find different ways to tune your systems, that data isn't as quite as

proprietary, your actual data and how you chunk it in the way that you send that

data to your clients that may actually be something that you care about securing.

Because we are sort of sending chunks of documents to specific users, we also have

much stronger ability to control which documents go to what users, which means

we can control how much our systems know.

If you're teaching a model about a number of different things that

some users should know and some others shouldn't, don't assume that

that will just stay in that model.

One of the things that's most exciting to me about refutable

augmented generation is that there's so many interesting capabilities

of, of really what's possible.

In one of my favorite papers discussing how you can develop these autonomous

agents to live in a village together.

They discussed how you can use retrieval augmented generation to develop

a brain like pattern where those characters in this simulated world,

perceive, plan, and reflect, and act.

By Sort of storing details about their day, they can sort of understand how

things are changing and make plans for them to be able to act accordingly.

While most systems are fairly rudimentary, where we're simply dealing with data

and chunking that data, there's a ton of possibilities in this space

and possible ways of being able to optimize this for different situations.

It's a really fascinating space, and I think it has significantly more

legs than fine tuning, which relies on you and your company and employees

to be able to understand how to curate data that an AI can understand.

If you've enjoyed this please consider following us on Disabled Discussion.

We have a podcast and a publication where we discuss different things about AI.

And we'll be posting more regularly as the weeks go on.

Thanks so much.