In every sort of AI application, success depends on data. It can be Spotify recommending a song or the new Apple Intelligence serving as your personal assistant for every task. The lifeblood of any application is data. If you have a good amount of quality data and computing resources, you're already ahead of everyone else in the GenAI race. But getting this data can be costly, time-consuming, and complicated - given that it's all real and existing data. That's why scientists thought of a great idea - using synthetic data to train LLMs and multimodal models.
Imagine trying to train a large language model (LLM) to understand human conversation. To fully train it, you need tons of text examples, but gathering all that from existing sources can lead to privacy issues and might not even capture the variety of language you really need. Synthetic data is a creative workaround that lets you generate artificial datasets that mimic real-world data. It helps save time, money, and labor to collect all that data, using AI to create what you need.
In this article, we'll explore how synthetic data is generated specifically for fine-tuning LLMs and multimodal models. We'll discuss the benefits, tackle some challenges, and the best formula to mix synthetic data with real human-generated data to build even better AI systems.
What is synthetic data, and why is it important?
Synthetic data is artificially created information that imitates real-world data. Imagine it as a realistic simulation generated by computers that captures the essential patterns and details of actual data without the extensive need for human intervention or using any personal or sensitive information. This means businesses can work with data that behaves like the real thing without compromising anyone's privacy and costing them a fortune.
Human-generated data vs. synthetic data
Let's start with human-generated data first. The real value of human-generated data lies in its depth. It captures the nuances of human expression, including idioms, emotional details, and cultural references, which are incredibly complex to replicate artificially. Also, if the annotators are experts in the field, human-generated data usually has higher quality. Furthermore, you have more control over the processes with human-generated data. You can set up a feedback loop, which lets you continuously refine the data based on what's working and what isn't. This way, you can quickly spot and fix any errors, make adjustments for biases, and keep improving the accuracy and relevance of your data. This adaptability means your data can evolve as conditions change or as new needs appear.
However, gathering this data isn't straightforward. It's often expensive and time-consuming to collect high-quality, diverse human-generated content. Additionally, there's the ever-present risk of biases, as this data mirrors the real world, which can sometimes present skewed or limited perspectives. So, while human-generated data is profoundly insightful, it's not without significant challenges.
Switching over to synthetic data, its main draw here is its scalability. Synthetic data can be produced rapidly and in vast quantities, offering a cost-efficient option for training models. Another significant advantage of synthetic data is privacy protection. Since it contains no real personal information, it sidesteps many privacy concerns associated with using real-world data, which is especially crucial in sensitive sectors like healthcare or finance. Additionally, when synthetic data is crafted thoughtfully, it can help counteract biases found in human-generated datasets, providing a more balanced representation.
However, synthetic data isn't without its flaws. Its effectiveness really depends on how well it mirrors the complexities of the real world. If it's poorly constructed, it might lead to models that don't perform well in practical situations. Plus, if the original human-generated data used to create synthetic versions have biases, these can unintentionally be magnified, which could introduce fairness issues. You should also consider that you have much less control over the generation of synthetic data than human-generated data. It can sometimes feel like a 'black box' where it's not clear how the data was derived.
Moreover, when training models on synthetic data, there's a risk of overfitting—that's when your model picks up too much of the noise in the training data instead of the actual patterns. This means when your model is tested with real-world data, it might not perform as expected because it was overly tailored to the synthetic scenarios.
As you can see, both data types have their strengths and challenges. Human-generated data brings a lot of depth and context but can be costly and sometimes biased. On the other hand, synthetic data offers scalability and ensures privacy but needs to be created carefully to maintain realism and quality. Often, the best approach is to blend both types. This way, you can capture the rich details and nuances of human experiences while also enjoying the efficiency and privacy benefits that synthetic data brings.
SuperAnnotate’s formula: Synthetic data + human data + model-in-the-loop
Our research at SuperAnnotate shows that combining synthetic data, human data, and a model-in-the-loop strategy often leads to the best results in training AI models. Here's a simple breakdown of how and why this works well:
Synthetic and human data together:
- Human-generated data offers depth and authenticity, capturing complex human behaviors that synthetic data might miss.
- Synthetic data offers scale and privacy, enabling the creation of large volumes of data without the cost or privacy concerns of human data.
By using both types of data, we can balance the strengths and weaknesses of each. Synthetic data can cover areas where human data is limited, and human data can ensure that synthetic data remains realistic and relevant.
Adding a model-in-the-loop:
- During training, a model like llm-as-a-judge continuously evaluates the data and provides feedback. This helps adjust the data generation process in real-time, ensuring the data remains relevant and effective for training.
- This method keeps the data fresh and aligned with the model's learning progress, improving the quality and effectiveness of the training process.
At SuperAnnotate, we integrate these approaches with top foundational model providers like Databricks. We provide the best toolset for building top-notch training data for improving models – combining the strengths of human-generated and synthetic data.
Using a mix of synthetic data, human-generated data, and a model-in-the-loop approach at SuperAnnotate, here's what we can achieve:
- Broad coverage: This allows us to capture a wide range of scenarios, from common to rare, using both real and synthetic data.
- Continuous improvement: Our system constantly updates itself based on feedback, ensuring our data always meets the needs of customers.
- Avoid overfitting: Our diverse data helps our models perform well on new challenges, not just familiar ones.
- Maintain privacy: By using synthetic data that contains no real personal information, we ensure privacy and compliance with regulations.
Synthetic data generation: IBM's and Nvidia's approach
Let's discuss how NVIDIA and IBM are generating synthetic data for training large language models (LLMs). Both companies have developed some interesting methods for creating data that help improve these models' performance.
NVIDIA's Approach
NVIDIA recently introduced Nemotron-4 340B, which is a set of open models designed for generating synthetic data. The main idea is that high-quality training data is essential for LLMs, but getting enough good data can be challenging.
Nemotron-4 includes three types of models: base, instruct, and reward. The Instruct model is particularly useful because it generates diverse synthetic data that mimics real-world scenarios. This helps ensure that the training data is varied and realistic.
Once the data is generated, the Reward model evaluates the outputs based on factors like helpfulness and coherence. This filtering process ensures that only high-quality synthetic data is used for training, making it more effective. Plus, these models are designed to work seamlessly with NVIDIA's NeMo framework, which makes it easier for developers to customize and fine-tune them according to their specific needs.
You can download Nemotron-4 340B on NVIDIA NGC or Hugging Face.
IBM's Approach
IBM has developed a new way to help chatbots learn better called large-scale alignment for chatbots (LAB). It's a method for generating synthetic data that will feed new knowledge to your chatbot without overwriting the model's already learned knowledge. The method uses a special system—a taxonomy—that helps developers pinpoint exactly what they want their chatbots to know and be able to do.
Think of this taxonomy as a big organizer that lays out all the skills and knowledge a chatbot should have in a neat, understandable structure. For instance, if a developer wants their chatbot to write an email summarizing a company's financials, the taxonomy shows all the necessary skills, like understanding financial terms and basic calculations.
Here's how it really works: another LLM, the teacher model, takes this organized knowledge and crafts high-quality, tailored instructions formatted as questions and answers. So, if you feed it financial statements and ask for help with profit calculations, it comes up with specific guidance. It even checks its own work, throwing out any instructions that don't make sense or are off-topic. The good instructions are then sorted into three categories—knowledge, foundational skills, and compositional skills—to ensure the chatbot learns things in a logical order.
IBM's LAB method updates and adds new information without messing up what the chatbot already knows. Using this method, IBM created a synthetic dataset with 1.2 million instructions and used it to train two new LLMs, Labradorite 13B and Merlinite 7B. These models ended up performing just as well, if not better, than some of the top chatbots out there, even those trained with much bigger datasets.
Conclusion
In wrapping up, it's clear that both synthetic and human-generated data have their unique strengths in shaping AI technologies. Human data brings depth and authenticity, while synthetic data adds scalability and privacy—essential in today's data-sensitive world. Combining these with a model-in-the-loop system really enhances our ability to train smarter, more efficient AI models. At SuperAnnotate, we blend these elements to develop AI solutions that are not just cutting-edge but also practical and reliable across various applications. This approach lets us push boundaries and tackle real-world challenges effectively.
