LLM Evaluation methodologies: A Deep Dive into LLM Evals

Large Language Models (LLMs) evaluations are as important as testing a unit of traditional software. This need is majorly as a rise in the widespread use of LLMs across applications which has raised the question of monitoring or measuring their performance. Before you deploy any AI-powered application, LLM evaluation is one of the crucial processes to do, and to do it you need LLM evaluation methodologies. As a developer, testing your LLM across specific use cases and criteria gives you the reassurance that it does what you designed it to do. If not, the testing allows you to identify performance gaps in the model’s workflow, and then you can adjust it accordingly.

Effective LLM evaluation leads to better AI-powered products and increased user trust. In one of our previous articles, we discussed the various metrics used in the evaluation of LLMs. In this article, however, our main focus will be on LLM evaluation methodologies where we will dive deep into the subject and see its correlations.

Let’s get started!

LLM model evaluation vs LLM System evaluation

Before we get into the deep aspects of this topic, we need to make the difference between LLM model evaluation and LLM system evaluation clear. Users often conflate these two terms because they are closely related. On the contrary, they are fundamentally different layers of assessment.

For starters, model evaluation focuses on measuring the performance of the LLM only, independent of how it is used within an application. Meanwhile, system evaluation focuses on how the model performs as a whole product and on the user experience it provides. To paint you a clearer picture, a model can perform excellently yet still deliver a poor user experience. Without proper system evaluation or model evaluation alone, this can happen.

In addition, effective evaluation strategies are a merge of both model evaluation and system evaluation. This gives you a picture of how the LLMs perform inside your actual product and not just by themselves. This way, you can test the system in its entirety with real prompts and real users and identify the problems that may affect your app’s usage after launch. Surely, the results obtained from model evaluation alone are not irrelevant, but a system-focused evaluation goes a long way in giving you an idea of whether or not your AI features are working the way you designed them to work. For most product teams, system-level evaluation is where they get the most insights.

What are LLM evaluation methodologies?

LLM evaluation methodologies are qualitative and quantitative methods used to assess how well your system performs when handling real-world tasks. It also assesses whether your system aligns with your application’s goals.

Human evaluation

This evaluation method is important for assessing hidden aspects of LLM outputs that automated metrics may miss. It captures user alignment, nuance, and tone in ways that automated methods may not replicate.

Methods involved in human evaluation include:

• Comparative judgement: here, reviewers compare two responses and choose the better one. It is easier and more consistent than simply assigning scores.
• Blind review: helps to reduce bias by hiding the model or versions that are similar to it during evaluation
• Rating: in this instance, reviewers score individual responses on a scale of 1- 5, which is based on pre-established criteria such as correctness or tone.

Human evaluation is considered the gold standard for assessing the quality of LLM outputs.

AI Evaluation

It is a quick and objective way to assess LLM performance, which involves the use of other language models. They are mainly metric-based and involve LLM evaluation metrics such as perplexity, BLEU and so on. This modern approach to LLM evaluation is gradually becoming the standard, especially when testing large amounts of LLM applications. The process includes:

• LLM judge: one LLM grades another LLM based on some pre-determined criteria
• LLM juries: multiple models evaluate the output of another model independently, and the results are aggregated to produce a unified final result.

Unfortunately, LLM-based evaluations are not perfect substitutes for humans as they may have bias, suffer from blind spots that cause them to overlook subtle failures, or have prompt sensitivity. Also known as Automated evaluation, this method also has some benefits, which include:

• It scales better than human reviews
• It can focus on more than one task at a time
• It can be updated by changing the prompts

To ensure that the results you get from LLM-based evaluations are trustworthy, you have to:

• Use them together with human checks, and not alone
• Use clear, task-specific prompts and scoring rubrics

Always consider them as a first attempt, like a draft, not the final results for your evaluation.

Adversarial evaluation

Adversarial evaluation involves subjecting the LLM to adversarial attacks to test them. The attacks are manipulated input data that force models to make incorrect predictions or release sensitive information. The aim of exposing the models to adversarial attacks is to expose vulnerabilities within the model that might not be obvious with other standard evaluation methods. After these are identified, developers can then create solutions that are more impregnable to attacks.

Adversarial evaluation is an LLM evaluation methodology that helps to identify potential risks, and mitigate them. Therefore, it is very useful for applications where reliability and security are crucial

What is a good LLM evaluation methodologies result?

You just finished evaluating your LLM application, results in hand and you are staring at it wondering if you have a good result. The best thing to do at that moment is to create a checklist. This checklist should contain the following and your LLM application must tick these boxes:

• Relevance or alignment: an LLM application must be relevant to your users’ needs or align with the tasks they want to carry out.
• Factual accuracy: the outputs generated must be factually correct and grounded in source content.
• Efficiency: the system must be able to operate optimally in real-life situations where cost, latency and scalability might affect its functions.
• Coherence and fluency: the generated output should be readable, well-structured and grammatically correct in the users’ language.
• Fairness and safety: eliminating bias is non-negotiable. The model must be bias-free and safe for everyone to use.

Conclusion

LLM evaluation methodologies are strategies that drive developers to identify flaws in an LLM application before it hits the market for use. After creating the LLM application, it is important that the vulnerabilities do not exist or are reduced to the point where attackers can not penetrate, but it is better if they don’t exist at all. Creating this tightly closed system prevents the theft or leak of users’ private information. Luckily, these LLM evaluation methodologies can reinforce one another, ensuring no stone is left unturned. The future of safe and reliable AI starts before, during, and after the development of an LLM application.

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