How to Evaluate the Performance of LLMs

As a professional in your field, you have just adopted and implemented a new functional hiring system that integrates an LLM which uses a speech-to-text model to record and document interviews with potential hires. You are happy with how much easier it has become to hire and even onboard new hires. However, just like you would carry out performance reviews on your new employees, you would also need to evaluate the performance of LLMs your company has just adopted.

Large Language Models are AI systems that have the ability to understand and generate human language by processing vast amounts of data. These models are built to scale and make AI tools and software more efficient. In addition, they have also become pivotal in shaping intelligent applications across various domains.

The big question is, have we ever stopped to evaluate their performance?

As the use of LLM systems is constantly scaling, especially in high-stakes sectors such as finance and healthcare, testing their output is crucial. That’s where LLM evaluation comes in.

In this article, we will discuss all the basics of how to evaluate LLMs and see why it is important.

Let’s get started!

What is LLM evaluation?

LLM evaluation involves a series of processes that deals with assessing the performance of LLM systems with the aid of different metrics, datasets, and tasks to measure their effectiveness.

LLM evaluation is not just a one time process or the repetitive loop of running LLM applications on a list of prompts. Rather, it is a step-by-step process that has a significant impact on the performance and longevity of the LLM application.

Why do we have to evaluate the performance of LLMs?

When LLMs are trained at greater scales with vast amounts of data, it is usually difficult to predict which task the model can do. Therefore, it is necessary to evaluate the performance of LLMs for their capabilities and operation. As such, LLM evaluations are useful for:

• Performance benchmarking: Evaluations help determine which tasks a model is and is not useful for.
• Monitoring implementation of AI ethics: LLMs can be influenced by human biases through training data during the development of the model. Through evaluation, potential inaccuracies and prejudices in model responses can be identified and adjusted accordingly. A focus on AI ethics helps safeguard against the technology perpetuating social inequalities and supports factual outcomes.
• New model development: The insights we can gain when we evaluate the performance of LLMs can guide the development of new models. It does so by helping researchers find ways to create new training techniques, model designs, or specific capabilities.
• Risk management in deployment: Mitigating risks such as data leakage, system manipulation, or operational failures.

What are the metrics used to evaluate the performance of LLMs?

Understanding the limitations and strengths of an LLM model is the first step to designing realistic but robust benchmarks and protocols. In designing these benchmarks and protocols, keep in mind that they must align with the evolving capabilities and use cases of LLMs. Numerous metrics are used for evaluation, some of which include:

Perplexity

This measures how well the model predicts a sequence of words or a sample of text. The more consistently the model predicts the correct outcome, the lower its perplexity score. A lower perplexity means the model is less perplexed, implying it is better at predicting the next word. Conversely, a higher perplexity indicates more confusion, meaning the model struggles to predict the next word correctly.

BLEU (Bilingual Evaluation Understudy)

BLEU assesses the quality of machine-generated text, particularly in translation tasks. It evaluates how closely the output resembles the ground truth majorly used with machine translation problems.

ROUGE (Recall-Oriented Understudy for Gisting Evaluation)

This is a set of metrics that evaluates the quality of automatically generated text, especially for LLMs that perform tasks such as text summarization and machine translation. It evaluates the quality of text summaries by comparing them to reference texts which in some cases are human-created ones. It ranks with a range between 0 and 1. Higher scores indicate higher similarities between the automatically produced summary and the reference text.

Accuracy

It calculates the percentage of correct responses in tasks such as classification or question-answering. It provides a simple and straightforward way to assess a model’s performance. Although it is very useful, accuracy alone may not be a sufficient metric in some cases such as in models that are open-ended in their generation. In such models, correctness may be highly subjective.

F1 score

This metric blends accuracy and recall into one metric. Compared to accuracy alone, it is a much more reliable metric to evaluate the performance of LLMs. F1 scores range from 0–1, with 1 signifying excellent recall and precision. This gives a more balanced metric which makes it important in models where both accuracy and recall are equally important.

The formula for calculating the F1 score:
F1 = 2 * (precision * recall) / (precision + recall)

Coherence

This is a measure of the logical flow, consistency, and smooth transitions of the generated text. There are two methods that can be used to measure coherence. They include:

Cosine similarity: often used in NLP, this metric measures how similar two sentences are by calculating the cosine of their angle and vector representations. Simply put, cosine similarity tells how similar generated texts are based on how similar the words they use are regardless of the number of times these words are actually used. The vector is the number that represents these words and the angle is the direction the words are pointing.

Human evaluation: this is simple, straight to the point, and does not involve too many technicalities. Here, human judges provide feedback on the coherence of LLM outputs by using surveys and scales. This factors in “humanness” and captures some aspects that cosine similarity might miss.

Recall

Measures the actual number of true positives, or correct predictions, versus false ones in LLM responses. It measures the ability of a metric to correctly identify all relevant instances it may encounter while working. It focuses on reducing the number of false positives to the barest minimum with the formula;

(TP + FN) / TP

Where,

TP = True positives, and FN = False negatives

Latency

Latency measures the model’s efficiency and overall speed. It helps to identify issues that may slow down the workflow, optimize user-experience, and ensure a high performance for users.

Toxicity

This is where the issue of AI ethics is fixed. This metric measures the presence of harmful or offensive content in model outputs. It uses numerical values to measure the number of harmful, offensive, or inappropriate content output of the LLM. This ensures that the LLM remains safe, unbiased, and aligned with human values.

Challenges in the evaluation of the performance of LLMs

Despite significant advancements, there are still significant challenges that we may come across when we want to evaluate the performance of LLMs. They include:

• Data contamination: Data contamination occurs when evaluation benchmarks overlap with the training datasets of LLMs. This leads to inflated performance metrics, distorted understanding of model capabilities, and overestimation of generalization and adaptability.
• Robustness: Adversarial inputs, such as minor perturbations or rephrased prompts, often lead to degraded performance.
• Scalability: Evaluation frameworks struggle to scale with increasingly larger models, such as GPT-4 and beyond, which involve billions of parameters. Moreover, the Computational and financial costs of evaluating models at scale are prohibitive.
• Ethical and safety concerns: Persistent societal biases in LLM outputs, evaluated using benchmarks like StereoSet and RealToxicityPrompts, remain unresolved. Models often fail to meet safety standards in sensitive applications like healthcare and education.

Addressing these challenges requires a combination of new metrics, adaptive evaluation strategies, and interdisciplinary research to ensure that LLM evaluation keeps pace with advancements in AI technologies.

Conclusion

When we evaluate the performance of LLMs on a timely basis, we are not only creating working models, we are constantly seeking and fixing problems that may impair the use of these models. Evaluation metrics lay down the foundation, and while some involve complex mathematical calculations, others require some human touch for a holistic approach.
Ultimately, the performance of an LLM may be affected by how much evaluation has been done over time. This highlights the need for timely evaluations of our LLM models.

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