Diversity in AI-Generated Text Through Variational Transformers and Latent Spaces

The importance of Diversity in AI-Generated Text

Human conversations are inherently diverse, we often avoid to repeat the same response verbatim, even when asked the same question multiple times. For example, if someone asks for a book recommendation, we might suggest different books based on mood, context, or even just make a random choice. We try to provide more information related to the book and through diverse response we wanted to motivate the potential reader to read that book.

"If you like sci-fi, try 'Dune'. If you prefer history, go for 'Sapiens'. For personal growth, 'Seven habits of highly effective people' is great!"

Modern transformer-based models, including GPT-4, have improved significantly in generating diverse text by using sampling techniques like temperature scaling, top-k, and nucleus sampling (Holtzman et al., 2020). However, these techniques still rely on manipulating probability distributions rather than fundamentally modeling variations in meaning.

Variational Transformers offer a more principled approach to text diversity by explicitly learning a latent space representation of diverse responses. Instead of picking the most probable response, they sample from a structured probabilistic distribution, ensuring that generated responses remain both coherent and diverse. This enables AI to generate contextually different responses while preserving meaning, just like humans.

Before going into details of how Variational Transformers work, let’s first understand the basic concepts of text data processing such as Tokenization, Embeddings, and Transformers.(These Concepts are based on following readings: Vaswani et al., 2017; Kingma & Welling, 2013; Bowman et al., 2016)

1. Tokenization – Breaking Text into Understandable Units

Before a machine can process text, it must break it into smaller parts called tokens: words, subwords, or characters. For example, the sentence:

"Artificial Intelligence is transforming the world."

Might be tokenized as:

["Artificial", "Intelligence", "is", "transforming", "the", "world", "."]

Modern NLP models like GPT-2 use Byte-Pair Encoding (BPE), which allows them to represent even new or rare words by combining smaller subwords (Sennrich et al., 2016).

2. Embeddings – Converting Text into Numerical Representations

Remember that machines do not inherently understand text; they process information as numbers—more precisely, a binary stream of 0s and 1s. Therefore, they must follow a systematic process to convert text into binary for computation and, conversely, translate binary back into text for human interpretation.

For this purpose, Tokenized words are converted into numerical vectors called embeddings, where words with similar meanings are placed closer together in a multi-dimensional space.

For instance, the words "King" and "Queen" will have embeddings that are mathematically close, reflecting their similarity in meaning (Mikolov et al., 2013). Embeddings form the foundation of context-aware text generation, allowing models to retain semantic relationships between words.

3. Transformers – Learning Context Through Self-Attention

Transformers revolutionized NLP by introducing the self-attention mechanism, allowing models to weigh the importance of different words in a sentence (Vaswani et al., 2017).

For example, in the sentence:

"She poured water into the glass and drank it."

The model learns that "it" refers to "water", even though there are other nouns in the sentence. This is possible because transformers assign higher attention weights to relevant words instead of treating all words equally. The ability to understand contextual dependencies is what makes transformers powerful, and they form the backbone of models like GPT-2, BERT, and T5 (Radford et al., 2019; Devlin et al., 2018; Raffel et al., 2020). However, there’s one major limitation: Transformers rely on probabilistic tricks to introduce diversity, but they still struggle to fully capture all the nuances and variations in meaning, especially in complex contexts.

4. The Problem – Why Transformers Struggle with Diversity

Modern transformers do introduce diversity through sampling techniques (e.g., nucleus sampling, top-k sampling), but these methods still have limitations (Holtzman et al., 2020):

• They introduce randomness externally, rather than learning a structured representation of diversity.
• They don’t capture nuanced variations in meaning, only different probabilities for next-word prediction.
• They still prioritize high-probability responses, making them prone to mode collapse, where diverse responses gradually disappear over many interactions.

For example, if you ask:

"Tell me a joke."

A transformer might generate similar responses most of the time because it selects from a probability distribution rather than truly learning semantic diversity. This is where Variational Transformers can change the game (Bowman et al., 2016).

5. Variational Transformers – Bringing True Diversity to AI

Variational Transformers do not simply adjust probabilities like traditional transformers. Instead, they introduce a stochastic latent space representation, which explicitly models the range of possible variations for a given input (Kingma & Welling, 2013).

This allows them to:

• Generate diverse responses based on a learned representation of variation.
• Learn structured clusters of meaning to improve contextual understanding.
• Introduce controlled randomness while maintaining grammatical accuracy.

Instead of sampling from a fixed probability distribution, Variational Transformers learn to encode diverse meanings into a structured latent space. The result is a model that naturally generates variation in responses while remaining contextually appropriate.

How Variational Transformers Differ from Standard Transformers

Standard transformers, like GPT-4, generate text by predicting the most probable next word based on prior context. While they introduce some level of diversity, this diversity is primarily artificially induced through probability tricks like top-k sampling and nucleus sampling. These techniques adjust the word selection process, but they do not fundamentally alter the way the model understands and generates meaning.

Variational Transformers, on the other hand, introduce true diversity by modeling variations in meaning at a higher semantic level, rather than just altering word probabilities. Instead of selecting words based on a static probability distribution, they encode an input into a latent space representation, allowing for probabilistic sampling from conceptual clusters. This leads to structured semantic variation, where responses differ in both wording and meaning.

Understanding Latent Space

To understand how Variational Transformers achieve this structured diversity, we visualize their latent space evolution using the following animation. The animation provides an intuitive representation of how the model organizes meanings into clusters during training. Each point in the latent space represents a possible response, and as training progresses, responses with similar meanings naturally group together.

These clusters indicate distinct response categories the model can generate. For example, a chatbot answering the question "How’s the weather?" may have different response clusters corresponding to factual reports, casual remarks, and humorous replies. Unlike standard transformers, which would rely on random word selection to induce variation, Variational Transformers can sample from different clusters, ensuring that the diversity is both meaningful and structured.

Additionally, we use other plots, such as the reconstruction loss curve and the KL divergence trend, to understand how well the model learns to balance diversity with coherence.

• The reconstruction loss plot measures how well the model can reconstruct its inputs, indicating how accurately it maintains semantic integrity in generated text.
• The KL divergence plot shows how the model regulates randomness, ensuring that generated responses remain diverse but still contextually appropriate.

These visualizations provide insights into how Variational Transformers generate diverse, structured responses while maintaining logical consistency.

Example: Book Recommendations

Consider a user asking: "Recommend me a book."

A standard transformer would generate a response based on word probability selection:

Outputs from a Standard Transformer

"You should read 1984." "I recommend The Catcher in the Rye."

While these responses differ, they follow the same semantic template, direct recommendations. Even with top-k sampling, the model would simply shuffle between different books without fundamentally changing the reasoning behind its choices.

A Variational Transformer, however, samples from structured meaning clusters in latent space, leading to contextually varied responses that differ not only in book choice but also in recommendation style:

Possible output from a Variational Transformer

"If you like sci-fi, try Dune. If you prefer history, go for Sapiens." "A timeless classic is 1984, but if you want something modern, try The Midnight Library."

The responses differ not just in words, but in structure and reasoning, showcasing true semantic diversity rather than superficial word-level variation.

References

• Vaswani, A., et al. (2017). Attention is All You Need. NeurIPS.
• Kingma, D. P., & Welling, M. (2013). Auto-Encoding Variational Bayes.
• Bowman, S. R., Vilnis, L., Vinyals, O., Dai, A. M., Jozefowicz, R., & Bengio, S. (2016). Generating Sentences from a Continuous Space.
• Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed Representations of Words and Phrases and their Compositionality. NeurIPS.
• Sennrich, R., Haddow, B., & Birch, A. (2016). Neural Machine Translation of Rare Words with Subword Units.
• Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language Models are Few-Shot Learners.
• Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.
• Holtzman, A., Buys, J., Forbes, M., & Choi, Y. (2020). The Curious Case of Neural Text Degeneration.
• Raffel, C., et al. (2020). Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer.