LLM Evaluation: The Crucial Step for AI Success

Author(s): Burak Degirmencioglu

Originally published on Towards AI.

The capabilities of Large Language Models (LLMs) are advancing every day, creating a revolutionary impact in the field of natural language processing. But how do we know if a model is “successful”? This is where LLM evaluation comes in. While “vibe testing” a practice of intuitively testing models was once common, it has now become essential to measure model performance in a reliable, consistent, and systematic way. LLM evaluation allows us to determine how well a model performs a specific task and to assess the quality and reliability of its outputs. This process not only accelerates model development but also ensures its reliability in real-world applications.

In this article, we will discuss why LLM evaluation is important, the transition from traditional to modern approaches in this field, online and offline evaluation metrics, model-based evaluation methods, and tools that facilitate the process.

Why Are Traditional Ways of Evaluating LLMs No Longer Sufficient?

Traditional metrics used for years to evaluate LLMs are now proving inadequate, especially for creative tasks like text generation. In the field of Natural Language Processing (NLP), statistical metrics like BLEU and ROUGE have long been used to measure the quality of generated text, particularly in tasks such as machine translation and text summarization. These metrics provide a quick and objective evaluation by measuring how much a model’s output overlaps with human-written reference texts.

BLEU (Bilingual Evaluation Understudy) is a metric that measures the overlap of words and word groups between a model’s generated text and a reference text. It is used primarily in machine translation evaluation and focuses on how similar the model’s output is to the reference translation.

ROUGE (Recall-Oriented Understudy for Gisting Evaluation) measures how much of the key information from a reference summary is contained in a generated summary. Widely used in text summarization tasks, this metric assesses how well the model recalls important words and phrases from the reference.

For example, when evaluating a translation model’s output, an output of “Hello world” gets a higher score if it is closer to the reference “Hello world.” However, these metrics struggle to capture more complex features like semantic accuracy or consistency. A text might have the right words but be out of context or nonsensical. Therefore, more advanced methods are needed to evaluate the complex and context-sensitive outputs of LLMs.

LLM-as-a-Judge: The Future of AI Evaluation

The limitations of traditional metrics have popularized a new approach: “LLM-as-a-Judge.” This method tasks one LLM with evaluating the output of another LLM. The output of a model is scored not by a human but by another model trained on specific criteria. This makes the evaluation process faster and more scalable. For example, to evaluate the response of a chatbot, the “judge” LLM is asked questions like “How helpful was this response?” or “How consistent was it?” and the model assigns a score based on these criteria. This approach can be used in various ways, from reference-based or reference-free evaluations that focus on a single output to pairwise evaluations that compare the outputs of two different models. Frameworks like G-Eval and DAG also make this approach more systematic.

Reference-based evaluation is when an LLM’s output is evaluated by comparing it to a pre-determined and validated reference answer. In this method, the judge LLM is given both the model’s output and the correct reference.

Reference-free evaluation, on the other hand, judges the model’s output without any predefined reference, based only on the given task and general quality criteria (such as fluency, consistency, and helpfulness). This method is more suitable for creative tasks or those with multiple correct answers.

G-Eval guides an LLM to score outputs based on specific criteria, increasing the consistency and transparency of the evaluation process. This framework can score a model’s answer by comparing it to both human input and reference answers.

DAG (Directed Acyclic Graph) creates a flow chart that puts a model’s output through multiple evaluation steps; this allows for a more comprehensive assessment by examining different aspects of a solution, such as its correctness and relevance. Unlike traditional metrics, these frameworks aim to better evaluate the semantic quality and context-appropriateness of the text.

What Are the Advantages of Evaluating a Model in an Online Environment?

Limiting the model development process to just offline metrics isn’t enough; understanding how a model performs in the real world is critically important. Online evaluation measures the true performance of models by directly monitoring their interactions with users. This evaluation includes user value, cost, and risk metrics.

User value refers to metrics that measure how useful a model is for users. This evaluates elements such as how efficiently a user completes a specific task or how satisfied they are with the model.

Cost metrics are used to measure the operational and financial expenses associated with a model’s operation and use. These metrics are helpful for understanding the economic sustainability of the model, especially in high-traffic systems.

Risk metrics are used to identify potential harm a model could cause to the user or the system. They track possible negative outcomes, such as a model generating toxic content, providing biased results, or causing security vulnerabilities.

For example, in a chatbot application, data is collected on how successful users are at a task (task completion), how often the model is used (user value), and how costly these interactions are (cost metrics). This data reveals the model’s value and potential issues in the real world.

How Can We Detect a Model’s Hallucinations or Errors?

The metrics used to evaluate LLMs fall into a few main categories. Basic Quality Metrics measure fundamental characteristics of a model’s output, such as its correctness, relevancy, and task completion.

Correctness measures whether the information a model generates is factual and free of errors. This metric ensures that the data or answers provided by the model are objectively accurate and verifiable.

Relevancy evaluates how related a model’s output is to the user’s question or task. The goal is to ensure the model produces direct and useful answers related to the topic.

Task Completion shows to what extent a model successfully fulfills the user’s request. This metric determines whether the model has completed all the steps of the requested task.

Faithfulness measures how well a summary or text generated by the model aligns with its original source. This metric ensures that the model correctly conveys information from the source text without distorting it or adding new information.

For example, you can check if an LLM’s summary accurately and completely reflects the information in the original text. This ensures the model’s output is consistent and faithful.

Responsible AI Metrics are also very important for determining a model’s reliability. These metrics examine a model’s tendency to produce false information (hallucination), its potential to create harmful or inappropriate content (toxicity), and whether it carries a bias against specific groups (bias).

Hallucination refers to a model’s tendency to present non-existent, false, or fabricated information as true. This metric is used to detect such erroneous outputs that compromise the model’s reliability.

Toxicity evaluates a model’s potential to generate harmful, profane, hate speech, or inappropriate content. This metric aims to ensure the model operates within safe and ethical limits for society.

Bias examines whether the model produces biased or discriminatory answers against specific groups (e.g., gender, race, or religion). This metric is critical for ensuring the model treats all user groups fairly and impartially.

What Tools Can Help Us Perform All These Evaluations?

There are many tools and frameworks developed to facilitate and automate the LLM evaluation process.

DeepEval, offered by Confident AI, is an open-source library that aims to measure model quality and responsible AI metrics.

Stax, developed by Google Labs, is a tool that simplifies and streamlines LLM evaluation.

Microsoft’s Experimental Platform is also specifically designed to evaluate both performance and responsible AI (RAI) metrics.

Platforms like Klu.ai provide comprehensive solutions that make it easy to compare different models and monitor their performance.

To summarize, LLM evaluation is a complex field with both technical and practical challenges. Accurately measuring a model’s success requires understanding not only the model itself but also its use case, objectives, and potential risks. Therefore, creating a comprehensive evaluation framework is critical to ensure that models provide accurate, safe, and consistent outputs.

In this article, we discussed why LLM evaluation is important, traditional and modern methods, different metric categories, and the tools that automate this process. If you want to learn more about LLM evaluation or share your own experiences, please feel free to comment.

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Published via Towards AI