"The great tragedy of science—the slaying of a beautiful hypothesis by an ugly fact." - Thomas Huxley
Evaluation is fundamental to any scientific endeavor. This quote reminds us that our ambitious goals and theories must be rigorously tested and often humbled by reality. This sentiment is especially relevant in the age of large language models (LLMs), where tremendous promise is only attained when we overcome the hallucination and requires rigorous evaluation at every step.
In this blog, we’re going to explore the topic of evaluation for LLMs, its importance and how we should approach it. Just as a hypothesis must be supported by evidence, so too must LLMs undergo thorough testing to ensure their reliability, accuracy, and alignment with human values. Let’s get started!
Evaluation is a complex science, with no single metric that can capture all aspects of a model’s performance. There are always trade-offs – objective vs. subjective, reliable vs. unreliable, inexpensive vs. costly, obscure vs. explainable. Ideally, we'd have a straightforward, reliable metric to pinpoint exactly how to improve our system, but this is challenging with LLMs due to the intricacies of human language.
The illustration below shows where some of the LLM evaluation techniques fall on the objective-subjective spectrum.
Figure 1. Illustration of the objective-subjective spectrum of evaluation approaches for LLMs.
On one end of the spectrum are quantitative metrics, such as accuracy, precision, recall, F1 scores, and BLEU scores. These metrics are derived from objective data and mathematical formulas, providing clear, repeatable, and unbiased assessments of a model's performance. They are particularly useful for comparing models, tracking improvements over time, and identifying specific areas of weakness.
Objective metrics are efficient, allowing for the evaluation of large datasets in a short amount of time without human intervention. They provide a consistent baseline for performance, making them indispensable for tasks where precision is critical, typically in various forms of classification and recommendation systems.
At the opposite end of the spectrum is human-in-the-loop testing, where human evaluators play a crucial role in assessing the model's outputs. This approach is essential for tasks that require subjective judgment, such as evaluating the naturalness of generated text, understanding of context, or appropriateness in a given situation. Human evaluators can provide insights that are difficult, if not impossible, to quantify, such as whether a model's response feels empathetic, is contextually relevant, or aligns with pre-defined norms.
Human-in-the-loop testing is particularly valuable when developing models intended for complex, high-stakes applications, such as customer service, medical advice, or legal consultations. While this approach offers a more holistic view of a model's capabilities, it is also time-consuming, costly, and prone to inconsistencies due to human subjectivity. Moreover, it is not always scalable, especially when dealing with large datasets. Let’s take a closer look at some of the most common techniques to understand what’s available when evaluating LLMs.
Evaluating LLMs is a multifaceted process that requires a combination of techniques to fully understand their capabilities and limitations. These evaluations can be broadly categorized into different types, each serving a specific purpose—from objective metrics that provide quantitative insights to human-in-the-loop assessments that capture qualitative nuances. By employing a diverse set of evaluation methods, we can ensure that LLMs not only perform well on standardized tests but also deliver reliable and contextually appropriate responses in real-world applications. In the following sections, we’ll explore these various evaluation types in detail, highlighting their unique roles and importance.
Objective metrics, sometimes referred to as reference-based metrics, will always play an important role in the evaluation of LLMs. These metrics provide a way to quantitatively assess a model’s performance, offering clear, repeatable, and unbiased evaluations. This is especially important in tasks where consistency and precision are critical, like in classification. Objective metrics are derived from mathematical formulas and statistical methods, allowing for a standardized approach to model assessment.
Common Objective Metrics:
Objective metrics are powerful because they offer consistency and scalability. They allow for the rapid evaluation of large datasets without human intervention, making them ideal for tasks where high throughput is necessary. However, they also have limitations. Objective metrics can often miss the nuances of human language, context, and meaning. For example, a high BLEU score does not necessarily mean that the generated text is meaningful or contextually appropriate. Similarly, a model might score quite well on accuracy but still produce outputs that are irrelevant or nonsensical in real-world applications.
While objective metrics are essential for evaluating LLMs, they often need to be paired with other methods, especially when the task involves complex, nuanced language use where human judgment is necessary to capture the full spectrum of performance.
Evaluating LLMs requires careful consideration of the datasets used in testing. Due to the generative nature of LLMs, it's essential to use testing sets that are not only high quality and reliable but also tailored to measure the specific skills and capabilities that are valuable for the task at hand. Many of the most effective evaluation datasets have been meticulously curated by the academic community and are widely recognized for their ability to benchmark model performance.
Table 1 contains a summary of the most common community benchmarks that are reflected in the Open LLM Leaderboard. These datasets typically focus on specific tasks, such as language translation, summarization, or question answering, and are designed to test the general capabilities of LLMs across a range of scenarios. To evaluate models effectively against these datasets with objective metrics, various techniques are employed, including standardizing output formats and applying post-processing methods to extract answers. These approaches help minimize the need for human intervention, enabling more efficient and consistent evaluation.
Table 1: Summary of common LLM Evaluation benchmarks (source).
Organizations fine-tuning LLMs for specific applications should focus on creating high-quality, task-specific datasets. These datasets allow you to track model performance over time and ensure that the LLM remains aligned with the desired outcomes. In industry settings, curating datasets that closely reflect the real-world applications of your system, you can achieve more accurate and meaningful evaluations, ultimately leading to a more reliable and effective LLM application.
Ultimately, your evaluation approach should be designed to measure how well the model performs the specific tasks it will encounter in the real world. In the rush to find shortcuts, some may be tempted to rely on less relevant or generalized datasets, but this can lead to suboptimal outcomes. By focusing your evaluations on the overall end goal (sometimes breaking it down into sub-goals), you ensure that the model is truly capable of solving the problem intended, rather than just demonstrating broad, generalized abilities.
When we start to reach the limitations of objective metrics and pre-existing datasets, human-in-the-loop evaluations become more important. Unlike objective metrics, which provide quantitative data, human-in-the-loop evaluations involve direct human judgment to assess the quality, relevance, and contextual appropriateness of a model's outputs. This approach, shown in Figure 2, is essential for capturing nuances that automated metrics might miss, such as the naturalness of language, emotional tone, or alignment with ethical standards.
Figure 2: Human-in-the-loop evaluation process.
These evaluations differ from the use of standardized datasets because they introduce subjective insights that are difficult to quantify but vital for ensuring the model's real-world applicability. Human evaluators can assess whether an LLM’s responses are not only technically correct but also meaningful and contextually appropriate in ways that automated systems cannot fully capture. This is particularly important in applications where the stakes are high, such as in customer service, healthcare, or legal advice, where the quality of interaction can significantly impact outcomes.
While human-in-the-loop evaluations are invaluable, they also pose challenges, being time-consuming, costly, and hard to scale. Despite these challenges, human-in-the-loop evaluations are crucial in specific scenarios, such as when gathering detailed insights about your system, debugging particular gaps in model behavior, or curating new evaluation datasets tailored to specific use cases. In these contexts, the human element is indispensable for ensuring that the model meets the desired standards of quality, relevance, and contextual accuracy. However, the labor-intensive nature of this approach highlights the need for more scalable alternatives, such as the LLM-as-a-Judge method.
The concept of using an "LLM-as-a-Judge" has emerged as a promising approach to partially automate human evaluations while still attempting to incorporate human-like judgment. The evaluation process is achieved by using one LLM to assess the outputs of another. This method offers a scalable solution for evaluating models on more open-ended questions, but it also introduces its own set of challenges and potential biases.
Figure 3: Overview of an LLM-as-a-Judge System.
The LLM-as-a-Judge approach uses a trained LLM to evaluate and score the outputs of another LLM, or even the same model, against specific criteria. The process typically works as follows:
While the LLM-as-a-Judge approach can significantly reduce the time and cost associated with human evaluations, it is not without drawbacks. All LLMs, including those used as judges, are susceptible to inherent biases that can skew the evaluation process. Here are a few examples summarized from this article:
Mitigating these biases involves strategies like swapping the positions of evaluation candidates and using few-shot prompting to provide reference examples during the evaluation process. However, it's important to note that human evaluations are still essential to complement the LLM-as-a-Judge approach, ensuring a more balanced and accurate assessment.
By integrating LLM-as-a-Judge with human-in-the-loop evaluations, we can leverage the scalability of automation while preserving the nuanced insights that only human judgment can provide. This hybrid approach can lead to more reliable and robust evaluation processes as LLM technology continues to evolve.
At the start of this blog, we highlighted that evaluation is a science in itself—an ongoing journey that involves figuring out what to evaluate, how to evaluate it, and how to incorporate newly developed techniques. To guide us on this journey, it's useful to take a brief detour into the world of software engineering, where the adoption of certain practices has dramatically improved the development process.
"The only way to go fast is to go well." - Robert C. Martin
In the early 2000s, software development was often characterized by long release cycles and unreliable products. The industry was in dire need of faster delivery, better quality, and improved operational efficiency. This led to the emergence of Agile methodologies and DevOps practices, which revolutionized how software was developed, tested, and delivered.
The core principles of DevOps that contributed to this transformation include:
These principles work together to create a high-functioning, reliable development process that drastically improves the software lifecycle.
Among these, Continuous Integration and Continuous Delivery (CI/CD) stands out when it comes to evaluation. CI/CD emphasizes the importance of continuously testing and validating software through automated processes. Every change to the system is rigorously tested to ensure that rapid iteration does not compromise the final product.
The DevOps mentality, particularly the CI/CD approach, is just as critical when developing applications with LLMs. LLM systems are inherently complex, and every modification—whether it's a tweak to hyperparameters, changes in prompts, adjustments to datasets, or even the incorporation of LLM-as-a-Judge techniques—can lead to significant shifts in output. These changes are akin to continuous iterations on a codebase, where each adjustment has the potential to impact the system’s overall performance.
To iterate quickly and effectively on these elements, robust and reliable evaluation processes are imperative. Just as CI/CD enables software developers to rapidly and safely deploy changes, continuous evaluation allows LLM developers to make necessary adjustments without sacrificing quality or introducing instability. By applying automated, consistent testing and evaluation methods, teams can ensure that each iteration—whether it's a new prompt structure or a refined dataset—genuinely improves the system.
Incorporating these iterative practices into your LLM development process is as crucial to improving your system as any other aspect of your workflow. Learning from DevOps and applying its principles to LLM evaluation can be the key to successfully developing and refining your models. Continuous testing, iteration, and automated validation should be foundational to your approach, ensuring that your LLMs evolve effectively and remain aligned with your goals.
When it comes to practical steps for creating effective tests and evaluation criteria for complex LLM systems, Hamel Husain’s case study on evals is particularly useful. It provides a detailed case study on improving an AI assistant for real estate by implementing unit tests, human and model evaluations, and A/B testing. The post offers practical guidance on setting up these evaluations, including using LLMs to generate test cases and automate assessments, and highlights how a well-designed evaluation system can also facilitate debugging and fine-tuning.
Evaluating LLMs is essential yet complex, requiring systematic approaches like those found in CI/CD to ensure ongoing improvement and reliability. By integrating systematic evaluations—whether through automated metrics, human-in-the-loop methods, or innovative techniques like LLM-as-a-Judge—teams can iteratively refine their models to meet real-world needs.
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