How do you measure the diversity of generated data with generative AI?

Quality and diversity are important, but they are not the same in the evaluation of generative AI. Whereas quality is concerned with integrity and realism, diversity is the richness of the generated data. Although there could be trade-offs, a model should ideally generate diverse and high-quality data.

Generative AI creates new, diverse data ??, but assessing this is complex. Employing the Fréchet Inception Distance score can help in understanding the similarity between natural and generated data ??? Precision and recall are pivotal for assessing the quality and diversity separately, while density and coverage metrics analyze the spread and reach of generated images in the data space ?? Novelty-based metrics focus on the uniqueness and variety of the generated content compared to existing data ?? Utilizing these metrics provides leaders with insights to refine the quality and performance of generative AI models ??

Diversity and quality in AI content are like spices in a dish; individually valuable, but together, they create a uniquely flavorful experience. Let's explore why this balance is essential: 1. To ensure Fairness: Prevent AI from amplifying existing biases or creating unfair outcomes 2. Mitigate Legal and Ethical risks: Ensure balance to mitigate potential legal and ethical risks.

This is akin to asking someone: Are you an expert in your subject, and also are you an equally strong expert in several other subjects. Either you build AI to give you strong output of one kind (and it may remain biased with its training), or you dilute the AI to give you several different outputs (and it may forever remain confusing). The question you must instead be asking is what do you want your AI to do. Imagine an insurance underwriting scenario and having an AI generate a range of quotations representative of the diversity of risk and possibilities - wouldn't it feel foolish? Also imagine a content gen AI giving you only logical arguments - wouldn't you feel inadequate? Keep focus. Clarify expectations.

In the realm of generative AI, the balance between diversity and quality is a crucial consideration. Often, there's a trade-off: as diversity increases, quality may suffer, and vice versa. For instance, in natural language generation, more diverse text may include grammatical errors or incoherent sentences. Achieving the right balance requires careful tuning of model parameters and objectives. This balance is reminiscent of a chef crafting a menu – too much variety can overwhelm the taste, while too little can bore the palate.

The key is evaluating both population-level diversity and sample-level diversity. At the population level, we want to ensure the generated data distribution covers the real data distribution well. The coverage metric quantifies the percentage of the real data distribution that is covered by the generated distribution. Values closer to 100% indicate good population diversity. However, high coverage does not guarantee fairness - the model could be generating only a narrow subset that happens to cover most of the distribution. This is where density comes in. Density measures how closely the density of generated data matches that of real data over the entire distribution. Values close to 1 indicate high population diversity.

Use measures like coverage, novelty, and mode collapse to compare generated data to original data or to the data itself in order to gauge its diversity. Use closest neighbour analysis, grouping, or classification, for instance, on photos of animals. To identify mode collapse in face photos, use the mode histogram, mode dropping, or mode seeking techniques.

With generative methods, it's crucial to ensure that the generated data doesn't introduce variations that are implausible within the given domain. Take, for instance, the medical imaging domain, where the application of generative methods could potentially yield images that lack anatomical realism. Such discrepancies could not only compromise the accuracy of diagnostic tools but also raise concerns regarding patient safety and treatment efficacy. Therefore, it is imperative to implement rigorous validation and quality control measures when employing generative techniques in domains like medical imaging to prevent these issues from arising.

For a reference point: Answerthepublic feels great in generating diverse ideas. Best part is that they don't even say they are AI yet. They just organize what we are talking about. However, any AI that is producing material that it hasn't really learnt from is, just like us, hypothesizing. It might do that better than us, but it still is just hypothesizing. The metric here to evaluate how effective an AI is in doing that is probably a simple count of tautological arguments it is able to produce. However by any metric you setup, I would imagine AI to give us so much diversity that our best chance to judge it still lies in how that diversity fits our worldview.

Diversity metrics are vital for generative AI models. In case of image related tasks, like faces, diversity ensures varied age, gender, ethnicity, and expressions. Metrics such as., clustering, coverage, and diversity scores will help us to assess novelty. Metrics help generate fresh and inclusive data, adding value to Generative AI applications.

Utilise the right measurements and techniques based on the format and kind of data to quantify data variety. For small-scale analysis, visual examination is subjective; for large-scale evaluation, quantitative analysis is objective. A hybrid strategy integrates both techniques for a thorough assessment.

The best way to test the diversity of test data is to apply domain knowledge to the problem. Using statistical sampling one can select subsets of generated data to test. Labelling services such as Appen can be used to assist with this process. Measures of diversity should be specified for the specific domain e.g. for image classification these could be: viewpoint, lighting scale, rotation, occlusion etc. A labelling task then needs to be created using the Appen system, for which a vast template library is available. Annotators then need to be trained. The generated data is then analysed, feedback provided and then one iterates through the process until you meet your measure thresholds.

Let's take an 'Art generation project', we usually begins with visual inspection for quick insights into diversity. As we scaled up, quantitative metrics like style entropy and color palette analysis will be introduced for further evaluation. However, we found the most effective approach to be a hybrid one—combining visual inspection by experts with quantitative analysis. Balance between subjectivity and objectivity always helps us in comprehensive understanding of diversity in generated art, and also in optimizing generative AI's output.

Several techniques can be used to enhance diversity in generative AI. Modifying model architecture or training objectives is a common approach. In text generation, you can use techniques like temperature scaling or nucleus sampling to control the diversity of generated text. In image generation, you can experiment with noise injection or altering the latent space dimensions. These methods are like adjusting ingredients in a recipe to achieve the desired taste and presentation.

The diversity of data in generative AI is difficult to quantify because of its complexity, availability, and interpretation. Without a clear set of criteria, it can be challenging to define and assess diversity because the data can be subjective, biassed, and multidimensional.

Tools like Word Embedding Association Test (WEAT) can help to identify biases related to gender, race, and other sensitive demographic attributes. If biases are picked up, the dataset can be augmented by generating synthetic examples that represent underrepresented groups or perspectives. This can balance the data and improve model performance on underrepresented classes. Before deploying NLP models in real-world applications, ethical reviews should be conducted to assess the potential impact on marginalized groups and to take steps to mitigate any harmful consequences. A final policy layer should be deployed to capture any potential mistakes the model makes, and to take corrective actions. One should always assume the model will fail.

Diversity as a creative criterion can surely be evaluated. ChatGPT Temperature offering some configurability is an evidence of this. However, diversity as a validation criteria is extremely difficult to achieve. For now, other than experts getting involved, I cannot think of being able to train AI. Understand this: even if you fed a fact-based dataset to train an AI, there is no guarantee that AI will make logically the same deductions that we may have made with the same set of facts. AI may be better or worse, but unless the deduction is comprehensible and practicable, there is no point taking this agenda forward.

Let's take an art generation project, art is multidimensional, defying easy quantification. training data is limited, biased tends to replicate known styles, hindering diversity. Art's subjectivity further adds ambiguity. To tackle this, we tend to diversify training data, enlist art experts for evaluation, and we incorporate user insights as well. Our experience underscores that, in art, diversity measurement requires a blend of quantitative metrics and human expertise, overcoming the challenges of data complexity, data size limitations, and subjectivity.

One of the challenges in measuring diversity is defining what "diverse" means. Diversity can be context-specific. For instance, in generating product descriptions, diversity might mean different ways of describing the same product's features. In medical imaging, diversity may refer to various anomalies and diseases. Additionally, existing diversity metrics may not always capture the full spectrum of diversity, and there's room for refinement. It's akin to a food critic struggling to define diversity when faced with cuisines from around the world.

Diversity measurement is a useful tool for optimising generative AI models. Strengths and weaknesses are identified with the use of this evaluation, and diversity can be improved by implementing techniques like data preparation, model architecture, and model training. These methods boost the output data's quality and diversity, which boosts AI models' overall performance.

In the context generative models for text its been shown that seeding the LLM/model with topics, short descriptions of what it needs to write about etc. can help with improving the diversity of generations. The reason is that without these seeds of information the LLM is prone to repeat the same set of generations because its output probability distribution for a given prompt isn't going to vary too much. The seed topics/instructions primes the LLMs output distribution to be different ensuring varied generations.

In real-world applications, diversity measurement is not just an evaluation, it is a crucial avenue for enhancement. Let's take a music generation project, we sought to elevate the diversity and creativity of AI-generated compositions. Initially, we need to improve diversity through data preprocessing. By curating a diverse dataset spanning various musical genres, eras, and cultures, we need to ensure the model had rich source material to draw from. General metrics in Music are genre entropy, chord progression variety, and melody novelty to quantitatively measure diversity.

Improving diversity often involves fine-tuning generative models. For instance, adjusting the model's architecture, training data, or objective functions can help strike a better balance between diversity and quality. Additionally, techniques like reinforcement learning can be used to promote diversity in generated content. It's similar to a chef tweaking a dish's recipe to add a unique twist and cater to diverse tastes.

Use Diverse prompting technique for improving the diversity of output from GenAI. It involves using prompts that are designed to encourage the GenAI model to generate diverse output.use prompts that are ambiguous or open-ended. This encourages the GenAI model to generate output that is more creative

Artificial intelligence research didn't begin with ChatGPT and generative AI. There are rich traditions of research and development across various domains, including natural language processing, computer vision, deep learning, and others. Within each of these domains, there's a wealth of information that applies to the challenges of diversity within generative AI. For example, in the natural language processing (NLP) field alone there are diversity metrics such as perplexity scores, n-gram ratios, latent semantic analysis, and numerous others. There's much to learn from existing research as we tackle generative AI challenges.

There is no one best way to measure the diversity of GenAI output. It will depend on the specific application. For example, if the GenAI model is being used to generate creative content, novelty may be a more important metric than coverage. If the GenAI model is being used to generate synthetic data for training other AI systems, coverage may be a more important metric than novelty.

Consider the ethical implications of generating diverse content, particularly in sensitive domains like text generation. Ensuring that diversity doesn't lead to generating harmful or biased content is crucial. In the culinary world, this would be akin to ensuring the ingredients used in a diverse menu don't pose health risks. In summary, measuring the diversity of generated data with generative AI is a nuanced endeavor, striking a balance between diversity and quality, employing diverse metrics and methods, addressing associated challenges, and continually striving for improvement. Just as a chef aims to create a diverse and delightful culinary experience, AI practitioners strive to generate diverse and high-quality content.