Large Language Models, or LLMs, have become a near-ubiquitous technology in recent years. Promising the ability to generate human-like content with simple and direct prompts, LLMs have been integrated across a diverse array of systems, purposes, and functions, including content generation, image identification and curation, and even heuristics-based performance testing for APIs and other software components.
While LLMs have proven their utility, there is a lot about this technology that is still relatively unproven. This tech is iterating day by day, and LLM evaluation has become a relatively common request for organizations looking for a system they can trust.
In this article, we’ll look at testing LLM models and integrations, including what LLM evaluation metrics are most useful for general evaluation and which testing models are helpful for more direct real world applications.
What is LLM Testing?
Firstly, let’s define what LLM testing even is. LLM testing is the process of taking a Large Language Model and evaluating it through a series of LLM testing tools. The results of these test scenarios – the LLM outputs – are then rigorously reviewed for everything from quality to speed in processing. Models such as OpenAI’s GPT, BERT, or Google’s PaLM, are all meant to be easy to integrate for quick generation and contextual output, but testing is required to figure out which of the hundreds of models currently on offer fit your form, function, and use case.
More to the point, this LLM testing ensures that models meet hard standards for quality, accuracy, reliability, and performance – but also that they meet soft standards around ethical considerations, energy and resource utilization, hallucinations, and more. You don’t want to deploy a model that has unproven quality metrics, so testing is critical.
As LLMs continue to grow both in size and relative complexity, this rigorous testing will only become more critical. These models are often trained on billions of data points and huge data sets, and while this increases their overall ability, it also amplifies the potential for everything from accidental hallucination to intentional bias injection.
Importance of LLM Testing
LLM testing is absolutely critical to ensure that your model is generating relevant responses whose actual output can be trusted. This testing should ensure a variety of metrics both qualitative and quantitative.
Key Reasons for LLM Testing
Accuracy
Most obviously, LLM testing should provide reasonable evidence that the content generated by the model is accurate based upon the given input and the training data available to it. This does not mean that every answer has to be unassailable or non-negotiable, but it should at the very least pass a quality test by human evaluators for relative accuracy. In other words, it doesn’t have to be perfect, but it can’t be outright wrong – factual correctness should be the primary focus of this aspect of testing.
Fairness
Bias can both be purposeful as well as accidental. If you only train your model with data that correlates high snow with high demand for eggs, your model might suggest that winter is the time where chickens are the most productive – this of course misses the cookie baking, cake making, and egg nog production that is actually underlying this deviation. Simple biases like this might be obvious, but when you have a data set being used to detect security or code vulnerabilities that considers poor practices standard because the training data simply has bad code in it, you have a foundational problem.
Performance
Performance of an LLM should be a core consideration of LLM capabilities. Testing applications should be able to look at an LLM’s performance both in discrete tests and generalized tests, pointing towards the relative model quality. Poor quality models tend to take a lot of resources to generate subpar output, while higher quality models tend to use a fairer amount of resources for decent to exceptional performance. This should be measured across multiple use cases and test cases.
Ethics and Compliance
Evaluating Large Language Models isn’t just about how quick an answer is achieved – it is also about a range of ethics and data protections that come with the generation of that information. For instance, some LLMs use the data that is inputted into the model to then train itself – this can of course be highly dangerous, as errant or purposefully harmful data can be used to poison the system. What about data retention for GDPR or CPPA protections? Are models training on copyright data to generate photos like the art of other artists? All of these ethical and compliance questions are paramount before adopting a model, especially in a corporate environment.
Optimization
Monitoring the ability to optimize your LLM is incredibly important. An amazing model that can’t be optimized will always lose out to a subpar model that can be improved, as LLMs are evolving and becoming better day by day. In order too improve accuracy and obtain better performance, LLMs should be able to scale both horizontally and vertically, but should also be able to be training on a variety of training regiments, including unit test cases, code generation prompts, and semantic similarity detection tests. A good model should be efficient in terms of latency, computational cost, and scalability, but most importantly it should be able to get better!
Key Components of LLM Testing
Effective LLM testing requires several key components focused on specific aspects of each model. The components help human evaluation by giving specific models or goals to hit – for instance, security testing might be concerned with the relative attack surface of the LLM, while natural language processing tests might be much more concerned with the human like text quality rather than the speed.
Let’s look at some key components of an effective LLM testing regiment.
Functional Testing
This testing validates that the LLM performs its intended task, whether that is text generation, text summarization, categorization, or code creation. Most importantly, it ensure that this task is done with accuracy and optimized performance relative to the given context and task at hand. This typically involves testing the responses to a wide variety of prompts to measure both the correct answer frequency as well as the model efficiency to obtaining that answer.
Bias and Fairness Testing
As mentioned previously, bias and fairness is a big concern for LLMs. This component of testing focuses on ensuring that the model is aware of biases from its data set, and utilizes appropriate and standardized datasets to draw answers from. In other words, this is a sort of evaluation testing to see if the answer provided is reasonable or if it is unnecessarily biased in some way.
Robustness Testing
This testing component tests the model’s ability to handle new or adversarial inputs. This allows for everything from handling ambiguous queries and edge cases to rejecting attempted prompt hijacking, SQL injection, and other direct malicious attacks against the model itself. This crosses over somewhat with performance and load testing in that in ensures the LLM cannot be adversarially used to increase resource and processing cost as an attack method.
Performance Testing
Performance testing measures the cold and hard metrics like latency, response time, and resource utilization across various conditions and prompts. This identifies bottlenecks of the systems driving the LLM, but in context of testing LLMs against each other, can also be used to identify potentially more effective models for your given use case and data set.
Testing Methodologies
The specific testing methodologies deployed for LLM testing is varied and depends largely on your specific goal, use case, the various industries and their regulatory compliances involved in the use of the LLM, and the overall state of the software systems and integrations with the core LLM offering.
Below are some common methodologies:
Unit Testing
This kind of testing focuses on the individual components or functionalities of the LLM. Examples of this testing involve verifying tokenization accuracy, measuring the efficacy of sentence or input parsing, or even validating individual transformer layers for in-transit queries. LLM applications are often hard to test in their totality, so this allows for testing practices that are focused on specific aspects of the model itself.
Integration Testing
Integration testing validates both the inner working of the LLM components (in a multi-LLM or complex model scenario) as well as the integration of the LLM into the system. Artificial intelligence is itself often composed of competing systems or techniques that need to work together in a specific way to generate useful data, and this type of testing allows for end-to-end testing from the first text inputs to the final outputs.
Regression Testing
Regression testing is a testing type that compares new versions of a model or its implementation against older models to ensure that no previously fixed issues or resolved integration errors reappear. This is extremely useful in an iterative environment, as it allows you to evolve without reintroducing weakness into the system. This is especially important in open source models, as these models typically use free or open data sources that are theoretically more vulnerable to attacks and poisoning.
A/B Testing
A/B testing compares two models or integrations of models to determine which performs better for a specific use case. This allows for models in a live environment to be compared with various controls and measurements in place, gathering real-world user or system feedback without fully exposing the system to a potentially malicious or broken LLM.
Stress Testing
This approach tests the model under extreme conditions. Testing applications powered by traffic simulation or real traffic replay allow you to test a model against simultaneous queries or inputs at scale. This is especially useful to test real-world applications and inject chaos, testing the model’s stability and failure points to ensure the model is usable even in difficult environments.
Metrics for LLM Testing
In order to measure performance, we need to set our metrics clearly. Qualitative and quantitative metrics must be used together to asset LLM performance, and some of these metrics include the following.
Accuracy Metrics
- BLEU (Bilingual Evaluation Understudy) – this metric measures the relative accuracy of machine-generated translations against reference translations, pointing towards accuracy and identifying potential biases towards previous poor translation data sets.
- ROUGE (Recall-Oriented Understudy for Gisting Evaluation) – this metric represents the ability of a model to recall the data that has been stored during the parsing of a piece of information and the efficacy of using that recall for summarization.
Bias Metrics
- Word Embedding Association Test (WEAT) – the WEAT test measures biases in models according to word associations. This is a symptomatic testing, meaning that it identifies the bias but does not necessarily surface the cause of that bias.
- DeepEval Bias Metric – this is a metric within DeepEval that measures specific biases related to classes of people.
Robustness Metrics
- Performance Drop Rate (PDR) – the drop in performance an LLM experiences when dealing with adversarial inputs.
- Measuring Massive Multitask Language Understanding (MMMLU) – a metric which tests the ability of an LLM to handle large tasks concurrently.
Performance Metrics
- Latency – the time taken between an input and the output response.
- Throughput – the number of queries that are processed per second.
- Resource Utilization – specific allocation and utilization of resources such as CPU processing speed, GPU memory, Random Access Memory (RAM), etc.
User-Centric Metrics
- User Satisfaction Scores – user sentiment analysis collected from surveys and user feedback processes to reflect the impact of the LLM integration on the overall use of the platform or system.
- Engagement Metrics – metrics such as click-through rates, read times, etc. for generated content, estimating the relative quality of engagement for such content at scale.
Tools and Frameworks for LLM Testing
There are a variety of tools and frameworks that can be used to streamline the testing process for LLMs. Below are just a handful!
Traffic-Based Testing via Speedscale
Speedscale is a powerful solution for capturing and replaying traffic. This is especially useful for LLMs, as you can measure the efficacy of an LLM backend for performance and service mocking with this real data. This leads to a more accurate estimation of the efficacy of the LLM, as you are testing against real users and real interactions at scale.
OpenAI’s Evaluation Toolkit
OpenAI provides an evaluation toolkit that employs both basic training systems as well as model-graded systems, which are systems that that compare the actual output of a model to the preferred factual answer, thereby estimating the overall efficacy of the LLM system.
Google’s What-If Tool
The What-If Tool (WIT) is a visualization tool that allows LLM developers and integrators to test the LLM against a variety of hypothetical situations, identifying everything from variable input to model manipulation.
Adversarial Robustness Toolbox (ART)
The Adversarial Robustness Toolbox (ART) is a security testing suite that allows for adversarial testing against poisoning, evasion, hijacking, and other direct attacks against models. it’s quite powerful, and allows for effective modeling from both an attacker and a defender point of view.
Best Practices for Testing LLMs
Testing LLMs requires adhering to a few best practices.
Define Clear Objectives
You can’t test without a clear and specific goal for that testing. Accordingly, deploy your tests with a specific goal and scope in mind. Don’t try and test everything at once – testing accuracy, fairness, and security all at once will give you a poor idea of any individual component. Test by function, use cases, and current objective for more actionable and direct feedback.
Use Diverse Datasets
Your model is going to represent the data that is knows – accordingly, use as many high quality models as possible to train your data, eliminating bias and reducing hallucination. Traffic replay is perfect for this, as it provides real data that reflects your actual user base and their data flow.
Incorporate Human Feedback
Automated systems are great for testing, but you need to combine metrics with qualitative feedback if you’re going to have high quality results. Users and domain experts are your source of truth – so use them heavily!
Continuously Monitor Performance
LLMs can start efficient and get bogged down over time as more data is ingested and processed. Make sure you are continuously monitoring your service and tracking the model performance post-deployment. This will help you detect solutions such as caching or data de-provisioning that can help improve your metrics across the system.
Iterate and Improve
Don’t just set and forget your LLMs – when you have data to act upon, validate that data and act upon it! Use your insights from testing to iteratively enhance your model’s performance and capabilities, and create an engine of consistent data and implementation improvement.
Conclusion
LLM testing is a critical element in the lifecycle of any large language model implementation; this process ensures efficient, accurate, fair, and robust systems at scale, and can help mitigate many of the potential pitfalls of integrating an LLM.
These metrics can vary depending on your specific use case, but regardless of what you are trying to test, data quality is going to be your primary focus. Speedscale can help ensure the quality of your data by providing you with an exact match to your real traffic and user data. This can help your testing techniques be set to a ground truth of actual user behavior, allowing you to engage in fine tuning and directed testing techniques that are informed by actual reality.
This can play a pivotal role in your testing and implementation – thankfully, Speedscale allows you to test this for free! If you’re ready to get started with Speedscale and see what real user data can do for your testing regiment, sign up for a free trial today!