Cheap, Fast, and Not Recommended: Using AI Only to Generate and Annotate Training and Fine-Tuning Data
High-quality data is the lifeblood of AI.
AI, ML, and LLM models need it and depend on it, to learn, evolve, and improve. As AI proliferates into every aspect of human life, the need for high-quality data will continue to increase exponentially.
But because it’s super hard for most AI builders to rapidly operationalize a global pool of domain experts themselves, it’s only natural for them to ask two simple questions:
Our AI models are smart, really smart - so why don’t we use those very models to generate high-quality data?
And why not use computers to annotate and prepare those datasets for training and fine-tuning?
At e2f, given that we serve some of the world’s largest AI and LLM builders, we’ve discussed these questions with our customers in depth, and we’ve had time to review some of the latest academic and scientific literature out there.
Here’s what we found.
Let’s start with using computers to generate (synthetic) AI data.
What is Synthetic Data Generation?
Synthetic data generation involves creating datasets that mimic real-world, human-generated data's properties and patterns. There’s no doubt that this is particularly useful when data is scarce or when privacy concerns restrict the use of real world data (e.g. medical data, health data, data as it relates to certain age groups, financial data etc.). Techniques like Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) have made synthetic data generation easy and efficient.
Why Synthetic Data Generation is Attractive
Volume and Scalability
As you can expect, computers can generate vast amounts of data quickly, addressing the needs of large-scale AI training processes. GANs can produce an almost unlimited number of data points, allowing models to train on diverse and extensive datasets.
Cost-Effectiveness
Synthetic data generation, as you can also expect, can be more cost-effective than hiring and operationalizing a pool of human domain experts to create high-quality, real-world data. The expenses involved in data collection, including labor, logistics, QA, project management, and more, can be significantly reduced with these techniques.
Privacy Preservation
Finally, synthetic data can help mitigate privacy concerns since it does not directly replicate real-world data but rather simulates it. This makes it a valuable tool in domains where data privacy is paramount, such as healthcare and finance, as previously mentioned.
Given these benefits, it’s understandable that synthetic data generation is of great interest to AI/LLM builders. But unfortunately, in life and elsewhere, there are no free lunches. Builders must also be aware of three significant downsides to relying on synthetic data.
Synthetic Data: Approach with Caution
Quality and Fidelity Issues
One of the significant challenges is ensuring that synthetic data accurately reflects the complex relationships and distributions found in real-world data. AI/ML and LLM models trained on low-quality, synthetic data inevitably lead to low-quality responses and dissatisfied users - at a minimum.
Bias and Representational Problems
Synthetic data can perpetuate or even amplify biases present in the original datasets. If the real data used to train the generative models contains biases, these will likely be reflected in the synthetic data, potentially leading to biased models. Without humans to scrub the data clean of biases or other toxic content, the models can run amok.
Privacy Risks
Despite its advantages, synthetic data is not entirely immune to privacy breaches. Techniques like linkage and attribute inference attacks can still exploit similarities between synthetic and real data to infer sensitive information.
Annotating or Labeling Data Sets
Now let’s talk about the second question builders are asking themselves - that of using computers and AI models to automate data labeling and annotation.
But before we get into the pros and cons, let’s briefly define data annotation.
What is Data Annotation?
Data annotation is the process of labeling data to train supervised learning models. This task is crucial for a wide variety of tasks such as image recognition, natural language processing, and speech recognition. Correctly labeled data helps models learn how to generate accurate responses. Data that is labeled incorrectly can lead to AI models mistaking muffins for chihuahuas, and vice versa.
Why Use AI Annotation
Using AI models to annotate and label data can be helpful in three ways.
Speed and Efficiency
Automated labeling tools can process and annotate large datasets much faster than human annotators, significantly speeding up the development cycle of AI models. This shouldn’t surprise anyone given how quickly computers can process any and all data.
Consistency and Objectivity
Computer models provide consistent and objective annotations, reducing the variability that can occur with human annotators across tasks, across projects, or just across different time frames. This consistency may be useful, especially when handling extremely large datasets for an extended period of time.
Scalability
Computer-based annotation systems can easily scale, handling vast amounts of data with minimal additional resources. As training data volumes explode, this can help control costs.
The Downsides of AI for Annotation
Just like we saw with (synthetic) data generation, there are significant downsides to using AI for data labeling. Let’s consider the three most crucial ones.
Accuracy and Quality Concerns
While automated tools are fast, they can struggle with complex tasks that require nuanced understanding. Human annotators excel in these areas, providing higher accuracy for tasks involving intricate details and contextual nuances.
For AI builders that are locked into an arms race, differentiation will increasingly come from how well their models handle nuance, and how well they handle expert domains. This is where machine annotations can overlook invaluable nuance and context, or struggle to understand integral calculus in Spanish, and so on.
Lack of Contextual Understanding
Computers often miss the subtleties and context that humans - at least most humans - can easily understand. This can lead to less accurate annotations, particularly in fields like sentiment analysis or medical diagnosis, or other critical situations, where context is crucial.
Dependence on Initial Training Data
The quality of automated annotations depends heavily on the initial training data used to develop the annotation models. If the initial data is synthetic and/or of low quality, AI can’t easily determine that the quality is poor, and the subsequent annotations will reflect these flaws, leading to poor model performance.
The Middle Path - A Hybrid Approach
At e2f, over the years, we’ve seen, first-hand, the pros and cons of using computers for AI data generation and data annotation.
That’s why we’ve found that a hybrid approach works best. It combines the speed and scalability of computer models with the accuracy and contextual understanding of human annotators. This hybrid approach, combined with best practices such as continuous evaluation and rigorous quality control, human oversight to validate and correct automated annotations, and regular fine-tuning to improve quality and performance, produces the highest-quality results.
Conclusion
The use of computer models for generating and annotating data in AI/ML/LLM models offers significant advantages in terms of speed, scalability, and cost-effectiveness. However, these benefits come with challenges related to data quality, bias, and privacy. A balanced approach that integrates both human and computer efforts can help mitigate these issues, ensuring robust, accurate, and reliable datasets for AI training and fine-tuning.
If you’re an AI/LLM builder that needs high-quality datasets turned around in 24-48 hours - or you’re a domain expert anywhere in the world that would like to serve the world’s AI builders as part of the e2f team, please contact us today.
