Unveiling Perplexity: Measuring Success of LLMs and Generative AI Models

Navigating AI success measurement // generated using Nightcafe

For language models and generative AI, success is often measured by the ability to generate coherent and contextually relevant text. But how can we quantify and compare the performance of these models objectively?

Enter Perplexity, a metric that has emerged as a valuable tool for evaluating the effectiveness of Language Models (LLMs) and Generative AI Models.

In this blog post, we will delve into the concept of perplexity and explore its relevance in assessing the quality and potential of these cutting-edge technologies. We will conclude with the limitations and shortcomings of perplexity and touch upon another metric, Burstiness.

Understanding Perplexity

Perplexity is a measurement that reflects how well a model can predict the next word based on the preceding context.

The lower the perplexity score, the better the model’s ability to predict the next word accurately.

Calculation Logic for Perplexity

Perplexity is calculated using average cross-entropy, which in turn is calculated using the number of words in the data set and the predicted probability of a word (target word) as per the preceding context. The preceding context is typically represented by a fixed-length sequence of words that precede the target word.

This length can vary depending on the specific model architecture and requirements.

To calculate the preceding context, you may start from the beginning of the sequence that comes before the target word, or include a certain number of words before it.

For example, if you want to predict the next word given the sentence “To execute this strategy, we would require”, the preceding context could be represented as “To execute this strategy, we would require” or a fixed-length window of the last 3 words, such as “we would require”.

Thus mathematically, the formula to calculate Perplexity is as follows:

Where H is the average cross-entropy, calculated as under:

Where N is the total number of words in the data set and P(w_i | w_1, w_2, …, w_{i-1}) is the predicted probability of the i-th word given the preceding context (w_1, w_2, …, w_{i-1}).

The choice of how many preceding words to include in the context window depends on the model and the task at hand. Some models may consider only the immediately preceding word, while others might incorporate a longer context window to capture more contextual information.

Significance for LLMs and Generative AI Models

Perplexity plays a crucial role in determining the success of LLMs and generative AI models for several reasons:

1. Enhancing User Experience: For product leaders and marketing professionals, generating high-quality content that resonates with users is essential. LLMs and generative AI models with low perplexity can contribute to creating compelling copy, chatbots with natural language understanding, and personalized recommendations that enhance user experiences.
2. Evaluation of Model Performance: Perplexity serves as an objective evaluation metric that enables product managers to compare different models and assess their performance. A lower perplexity indicates a model’s higher proficiency in capturing language patterns to generate coherent text.
3. Fine-tuning and Iterative Development: By tracking perplexity during model training, developers can gain insights into the impact of different techniques, architectures, or datasets. This allows them to fine-tune the models iteratively, leading to continuous improvement in model performance.
4. Benchmarking Industry Standards: Perplexity provides a benchmark to compare LLMs and generative AI models against established industry standards. It facilitates communication between product managers and stakeholders by providing a quantitative measure of the model’s capabilities, helping set realistic expectations and demonstrating the competitive edge of the product.

Shortcomings and Limitations of Perplexity

While perplexity is a useful metric for evaluating LLMs and generative AI models, it also has certain shortcomings and limitations to consider:

1. Model Vocabulary Could Unfairly Influence Perplexity: Perplexity heavily relies on the model’s vocabulary and its ability to generalize unseen words. If a model comes across words or phrases that are not present in its training data, it may assign high perplexity scores even when the generated text makes sense. This limitation highlights the challenge of training vocabulary and seeks the need for models to avoid vocabulary-driven unfairness in Perplexity score measurement. The models should be well-trained so that their vocabulary can accommodate rare or adjacent terminologies belonging to their domains.
2. Lack of Subjectivity Consideration: Perplexity is an objective metric that does not account for subjective factors such as style, creativity, or appropriateness in specific contexts. A model with a low perplexity may generate text that is technically accurate but lacks the desired tone, voice, or level of creativity required for certain applications. Human evaluation or additional metrics should be employed to assess these subjective aspects.
3. Contextual Understanding: Perplexity primarily focuses on the prediction of the next word based on the preceding context. However, it may not capture the model’s overall understanding of the broader context. This limitation can lead to instances where the generated text appears coherent based on word prediction but lacks general contextual relevance.
4. Language Ambiguity and Creativity: Perplexity does not capture the model’s ability to handle language ambiguity or generate creative and novel outputs. Language often contains multiple valid interpretations, and a low perplexity score does not guarantee that the model can handle ambiguity effectively or produce creative responses.
5. Domain Specificity: Perplexity is sensitive to the domain and distribution of the training data. Models trained on specific domains may achieve lower perplexity within their domain but may need help generating text outside their training context. Adapting models to new domains or capturing the broader diversity of language remains a challenge for perplexity-based evaluation.
6. Overfitting and Generalization: Perplexity can be affected by overfitting, where a model performs exceptionally well on the training data but struggles with generalizing to unseen or real-world examples. Models that achieve low perplexity scores on a specific dataset may not perform as well on diverse inputs or in practical scenarios. Care must be taken to ensure that the model’s performance is not solely optimized for perplexity at the expense of real-world effectiveness.

Perplexity provides a valuable but limited measure of success for LLMs and generative AI models. While it offers an objective evaluation metric, it may not fully capture contextual understanding, subjective aspects, and domain adaptation.

Ongoing research aims to address these limitations and enhance evaluation methods. By adopting a holistic approach, we can maximize the potential of these technologies and drive innovation in AI applications. In addition, employing a complementary metric like Burstiness can
help to better measure the success of LLMs and generative AI models. Burstiness measures the model’s ability to generate text that is not only coherent but also exhibits bursts of novelty and creativity.

Burstiness assesses the model’s capacity to produce unexpected and unique outputs, capturing its capability to go beyond predictable patterns and generate engaging and diverse content.

By combining perplexity, which evaluates language predictability, with burstiness, which assesses creativity and novelty, a more comprehensive evaluation of the model’s success can be achieved. This dual approach provides a balanced perspective, accounting for both fluency and innovation in the generated text. We will deep-dive into burstiness and the combination of perplexity and burstiness for model evaluation in the upcoming blog posts.

Providing balance // generated using Nightcafe

As we navigate this exciting frontier, a holistic evaluation approach will be essential to ensure the success and real-world effectiveness of these transformative technologies.