Fine Tuning ChatGPT is a Waste of Your Time
Summary
TLDRThe 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
🤖 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.
🔍 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.
Mindmap
Keywords
💡Fine Tuning
💡AI Model
💡Context Window Problem
💡Overtraining
💡Retrieval Augmented Generation (RAG)
💡Large Language Models (LLMs)
💡Data Intensive
💡Representative Data
💡Corpus
💡Security
💡Autonomous Agents
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.
Посмотреть больше похожих видео
"I want Llama3 to perform 10x with my private knowledge" - Local Agentic RAG w/ llama3
Fine Tuning, RAG e Prompt Engineering: Qual é melhor? e Quando Usar?
RAG vs. Fine Tuning
Introduction to Generative AI
Building Production-Ready RAG Applications: Jerry Liu
Prompt Engineering, RAG, and Fine-tuning: Benefits and When to Use
5.0 / 5 (0 votes)