Large Language Models (LLMs) are specialized natural language processing (NLP) models trained on huge datasets capable of generating human-like responses to various queries. They first sprung to the limelight with the introduction of ChatGPT in December 2022 and have ever since been molded to fit various use cases.
The architectures are fine-tuned to fit specific use cases and then deployed to production. However, since these models have become a vital part of many development pipelines, it is crucial to thoroughly validate LLM applications before moving to production.
This article will discuss why LLM testing is so important and the key factors to look out for while validating a large language model.
Why is LLM Testing Important?
Although LLMs are a subset of AI models, they fundamentally differ from conventional AI. This is because LLMs have an unstructured output format (Text), making it impossible to evaluate using conventional techniques and metrics. Traditional models like linear regression, image classifiers and object detectors can be validated using metrics like accuracy, R2 score, IOU, Precision, Recall, etc., but these metrics do not work for LLM outputs.
An LLM can structure its response in various ways, each of which can be correct. Take the following prompt as an example
“Fill in the following blank. ‘Albert Einstein was born in _____’”
An LLM may respond with the following answers:
- “Albert Einstein was born in 1879” or
- “Albert Einstein was born in Germany.”
Both outputs are different, yet they are technically correct. In such ambiguous scenarios, an LLM would require additional prompting and context to get the desired result. Due to the unstructured nature of the response, no concrete metric can define the LLM’s performance.
Due to their wide adoption, LLMs are being transformed into vital applications such as chatbots for e-commerce websites. If these applications output incorrect or irrelevant results, it could lead to a loss of business. Building LLM applications for production requires manual and laborious testing against several relevant prompts to ensure the model does not start hallucinating.
Let’s discuss some LLM testing methodologies to ensure a production-ready model.
Validating LLMs for Production
It is difficult to determine whether an output from a large language model is to be considered appropriate. Let’s discuss a few benchmarks that can be used to test output quality.
1. Output Consistency
LLMs are designed to include a randomness/creativity factor in their output. LLM engineering includes a hyperparameter called temperature, which controls the randomness of the output. A high-temperature model would try to output unique and creative responses with every iteration, while a lower-temperature would mainly stick to the context of the prompt.
The problem with such randomness is when the model starts deviating from the prompt requirement. This would mean the model may not always output a relevant or correct response.
LLM validation must include rigorous testing to ensure that the output deviation is never such that it becomes incorrect. This would include testing a single prompt at various times to ensure consistency.
2. Robustness to Prompt Variation
A single sentence can be constructed in many ways while its meaning and understanding remain the same. For example, consider these sentences:
“What will the weather be like tomorrow?”
“Will I need an umbrella tomorrow?”
Both sentences ask for the weather conditions the next day, but while the first question is straightforward, the second requires slightly more understanding. A production-ready LLM must be able to gather such context and understand what the user needs from the output.
This assessment can be conducted by carefully constructing matching prompts to ensure the LLM does not confuse itself. Another technique for prompt validation involves using another LLM to restructure a prompt while maintaining its original semantics. The original and rephrased prompts are input into the model, and the outputs are compared to see whether the model understands the semantics of both.
3. Prompt Overfitting
In many practical applications, pre-trained LLMs are fine-tuned on carefully engineered prompts. For example, a brand selling beauty products may fine-tune a model to advise its customers regarding its products.
However, a common issue with prompt tuning is that the model might overfit the prompts provided during training and fail to recognize variations. This is a problem since the model will face all sorts of variations. Testing for overfitting is similar to our last point, where we create multiple variations of the same prompt. The model is tested on these variations to ensure it understands the context well enough to output relevant responses.
4. Testing for Compliance
Another problem with using pre-trained models is that you are unaware of their training data. A key practice in LLM programming is to ensure that it does not offer any opinion on sensitive and controversial prompts. Before being released to production, an LLM must be validated for compliance with ethical and societal guidelines. Any racial or ethical biases must be eliminated so the model remains fair to all users. Compliance testing is important not only because it provides a fair experience to users but any data compliance-related loopholes can land an organization in lawsuits worth millions of dollars.
5. Cost Optimization
The costing structure of an LLM is divided on a per-token basis. An LLM token is essentially the fundamental building block of the model. It can be a word, a group of words, or a phrase, depending on the LLM programming. The higher the number of tokens in an input prompt, the more processing the model requires and the more it costs.
When deploying the model, its architecture must be optimized for cost-effectiveness. The slightest tweak in the architecture can increase the cost manifold. Hence, all changes must be monitored and tested for model efficiency.
LLM Validation with Deepchecks
LLMs have become the new technology trend and are widely adopted across various industries. The models are fine-tuned to fit unique use cases and explore endless automation possibilities. However, a major problem with language models is that they cannot be validated straightforwardly. Their unstructured outputs must be manually tested for accuracy and relevancy before being released to production.
A few effective LLM testing methodologies include testing for output consistency, prompt understanding, and checking for biases. These techniques ensure that the model’s output aligns with the user’s expectation no matter how prompted. For those who wish to go further into LLM evaluation, some additional techniques include ReLM and Empirical Evaluation. These validations are a manual and tiresome process, but they ensure that the model in production is accurate, unbiased, business-friendly, and creates a safe AI environment.