Hey there, fellow AI enthusiasts! Today, we’re going to talk about a very serious topic that also happens to be quite hilarious: how ChatGPT data can poison open-source models.
Yes, you heard it right! We’re going to talk about how that fancy language model you’ve been using for all your chatbots might actually be harming them.
So, sit back, relax, and let’s dive in!
What is ChatGPT, and why do we use it?
Before we get into the nitty-gritty of the issue, let’s first talk about what ChatGPT is and why it’s so popular. ChatGPT is an AI language model developed by OpenAI, and it’s capable of generating human-like responses to a wide range of queries.
It’s been used in a variety of applications, including chatbots, language translation, and even creative writing.
One of the main reasons why ChatGPT is so popular is that it’s open-source, which means that anyone can use it and build on top of it.
This has led to the development of many open-source language models that are based on ChatGPT’s output. The idea is to fine-tune these models with ChatGPT examples so that they behave more like ChatGPT.
Why using ChatGPT data for training can backfire?
But here’s the thing: using ChatGPT examples for fine-tuning open-source models can backfire.
According to John Schulman, the co-founder of OpenAI, this approach can significantly exacerbate the problem of hallucinations in open-source models.
Hallucinations, in this case, refer to situations where the model generates incorrect or nonsensical responses.
The problem with using ChatGPT examples for fine-tuning is that it can lead to the model learning incorrect information.
For example, if the dataset contains a question like “What’s the name of the Han Solo spin-off movie?” and the answer “Solo,” a model that already knows the answer will learn to give correct answers. However, a model that doesn’t know the answer might end up giving incorrect answers or even hallucinate an answer.
And the worst part is that it’s not always clear what information is contained in a language model like ChatGPT.
This means that a dataset generated by ChatGPT can lead to a model learning incorrect information, which can then lead to it giving incorrect or nonsensical responses.
How OpenAssistant can help
So, what’s the solution to this problem? Well, according to Schulman, reinforcement learning with or without human feedback is one way to correct problematic behavior. OpenAssistant is a project that’s taking this approach.
Unlike other open-source models that use instructional tuning, OpenAssistant has collected its data with human volunteers and plans to add reinforcement learning to the models.
The human dataset generated by OpenAssistant avoids the problem of reproducing the biases and quality constraints of the source model. If the outputs of these models then permeate the Internet, a kind of echo chamber could emerge in which open-source models amplify their own errors and biases.
So, there you have it, folks. The use of ChatGPT examples for fine-tuning open-source models can backfire and lead to the model generating incorrect or nonsensical responses.
But fear not! There are solutions, such as reinforcement learning with human feedback, that can help correct problematic behavior.
As always, it’s essential to be aware of the potential pitfalls when working with AI models and datasets.
By staying informed and using best practices, we can build better, more reliable models that can truly make a difference in the world.
Instruction tuning is a technique used to fine-tune language models with specific examples or prompts. It involves training a model on a dataset containing questions with answers or requests to summarize text with separate summaries. The goal is to create a chatbot that can make as few mistakes as possible and knows when it is stuck.
Alpaca is a language model developed by Stanford researchers that is fine-tuned with ChatGPT examples. It significantly outperformed other models in tests and has been reproduced in many open-source projects as a kind of Alpaca formula.
OpenAI warns against simple instruction tuning because it can lead to models producing incorrect responses and even hallucinations. Hallucinations occur when a model is trained on a dataset that contains knowledge that the original model did not have.
The problem with using ChatGPT data to fine-tune open-source models is that it can exacerbate the problem of hallucinations. ChatGPT is a much larger model with more knowledge than most open-source models, so a dataset generated by ChatGPT can lead to thousands of examples where a model learns to give an answer even though it does not know the correct one.
Open Assistant is a project that collects data with human volunteers and plans to add reinforcement learning to its models. This approach can help correct learned problematic behavior in models, unlike the currently available open-source models that only use instruction tuning.
The potential danger of using ChatGPT-generated data in open-source models is that it can create an echo chamber where models amplify their own errors and biases. If the outputs of these models permeate the internet, a kind of feedback loop could emerge where errors and biases are reinforced and difficult to correct.