03/28/2024 | Press release | Distributed by Public on 03/28/2024 07:22
In a previous blog post, I discussed the legal and customer trust concerns associated with generative AI models. In this blog, I will explore the challenges of achieving complete avoidance of parroting, consider whether parroting can be quantified as a metric, and highlight some potential areas for research.
Achieving Complete Avoidance of Parroting
Complete avoidance of data memorization in generative models, particularly in large-scale machine learning models like deep neural networks, is challenging for several reasons:
Can Data-Parroting be Quantified as a Measurable Metric?
Developing a quantifiable data-parroting metric involves defining similarity thresholds at which copying/parroting becomes a concern. Human-in-the-loop experiments with experts (designers, legal, etc.) may help determine these thresholds. Understanding the nuances in setting these thresholds is vital for effective copyright protection.
Potential Research Problems
Diversity Assessment in Datasets: A key challenge in preventing data parroting is the curation of diverse and large datasets. With long-tailed data distributions potentially degrading diversity, developing robust measures for dataset diversity is crucial. Addressing this challenge is essential for balancing diversity and fidelity in generated outputs.
Preventing Replicas During Training: Incorporating regularizers during training could prevent data replication, although excessive regularization might degrade output quality. Finding the right balance between learning from training data and ensuring diversity is critical. While differential private learning has been explored, its effectiveness remains debatable.
Detecting Replicas After Training: Approaches like contrastive or general representation learning can be leveraged for detecting replicas. However, these methods may be impractical for large datasets, necessitating the development of faster, more efficient techniques. The challenge lies in comparing a generated sample against billions of training samples, highlighting the need for scalable solutions.
In subsequent posts, we will delve into the specific approaches we use at Autodesk to detect and prevent data parroting in our generative models. This exploration will include a deeper dive into the technical and legal frameworks that support the ethical and responsible use of generative AI technologies.
The information provided in this article is not authored by a legal professional and is not intended to constitute legal advice. All information provided is for general informational purposes only.
Saeid Asgari is a Principal Machine Learning Research Scientist at Autodesk Research. You can follow him on X (formerly known as Twitter) @saeid_asg and via his webpage.