LLM evaluation: Why testing AI models matters
30 October 2024
Authors
Amanda McGrath Writer
Alexandra Jonker Editorial Content Lead

Imagine a company hires a new employee. Their resume is excellent and they complete all of their tasks quickly and efficiently. Their work is technically getting done—but is it getting done well? Is it high quality, accurate and reliable?

As with any new hire, managers take time to review their work to make sure that it meets company standards and performs appropriately. As artificial intelligence (AI) plays a larger role in business output and decisions, companies need to do the same for LLMs.

Large language models (LLMs) are foundation models that are trained on immense amounts of data and used for tasks that are related to understanding and generating text. For example, this type of AI system is especially useful for work such as content creation, summarization and sentiment analysis.

LLMs revolutionized the field of natural language processing (NLP) and brought generative AI into the public eye in new ways. OpenAI’s Chat GPT-3 and GPT-4, along with Meta’s Llama, are the best-known examples, but a wide range of LLMs is used in various domains. LLMs power AI tools such as chatbots, virtual assistants, language translation tools and code generation systems.

As LLM applications are adopted more broadly, especially for use in high-stakes industries such as healthcare and finance, testing their output is increasingly important. That’s where LLM evaluation comes in.

What is LLM evaluation?

LLM evaluation is the process of assessing the performance and capabilities of large language models. Sometimes referred to simply as “LLM eval,” it entails testing these models across various tasks, datasets and metrics to gauge their effectiveness.

Evaluation methods can use automated benchmarks and human-led assessments to find an LLM’s strengths and weaknesses. The process involves comparing the model's outputs against ground truth data (information that is assumed to be true) or human-generated responses to determine the model's accuracy, coherence and reliability. The results of LLM eval help researchers and developers identify areas for improvement. Evaluation processes are also a central component of large language model operations, or LLMOps, which involves the operational management of LLMs.

Why is LLM evaluation important?

As LLMs play greater roles in everyday life, evaluating them helps ensure that they are operating as intended. Beyond technical needs, LLM eval also helps build trust among users and stakeholders.

LLM evaluation can help with:

  • Model performance
  • Ethical considerations
  • Comparative benchmarking
  • New model development
  • User and stakeholder trust
Model performance

LLM evaluation shows whether the model is performing as expected and generating high-quality outputs across its tasks and domains. Beyond basic functionality, evaluation can reveal nuances of language understanding, generation quality and task-specific proficiency. It can also pinpoint potential weaknesses, such as knowledge gaps or inconsistencies in reasoning, which allows researchers and developers to better target improvements.

Ethical considerations

As they are developed, LLMs are influenced by human biases, especially through training data. Evaluation is one way to identify and mitigate potential prejudices or inaccuracies in model responses. A focus on AI ethics helps safeguard against the technology perpetuating social inequalities and supports factual outcomes.

Comparative benchmarking

LLM evaluation allows people to compare the performance of different models and choose the best one for their specific use case. It offers a standardized means of comparing results from raw performance metrics to factors such as computational efficiency and scalability.

New model development

The insights that are gained from LLM evaluation can guide the development of new models. It helps researchers find ways to create new training techniques, model designs or specific capabilities.

User and stakeholder trust

LLM evaluation supports transparency in development and builds confidence in output. As a result, it helps organizations set realistic expectations and foster trust in AI tools.

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LLM model evaluation vs. LLM system evaluation

While closely related, LLM evaluation and LLM system evaluation have distinct focuses.

LLM evaluation (which can also be called LLM model evaluation) assesses how well a model performs. It looks at the core language model itself, focusing on its ability to understand and generate text across various tasks and domains. Model evaluation typically involves testing the model's raw capabilities. These capabilities include its understanding of language, the quality of the results it generates and task-specific performance.

LLM system evaluation is more comprehensive and provides insights into the end-to-end performance of the LLM-powered application. System evaluation looks at the entire ecosystem that is built around an LLM. This effort includes scalability, security and integration with other components, such as APIs or databases.

In short, model evaluation centers on making sure the LLM works for specific tasks, while system evaluation is a more holistic look at its overall use and effectiveness. Both are essential for developing robust and effective LLM applications.

LLM evaluation metrics

The first step in LLM eval is to define the overall evaluation criteria based on the model’s intended use. There are numerous metrics that are used for evaluation, but some of the most common ones include:

  • Accuracy
  • Recall
  • F1 score
  • Coherence
  • Perplexity
  • BLEU
  • ROUGE
  • Latency
  • Toxicity
Accuracy

Calculates the percentage of correct responses in tasks such as classification or question-answering.

Recall

Measures the actual number of true positives, or correct predictions, versus false ones in LLM responses.

F1 score

Blends accuracy and recall into one metric. F1 scores range 0–1, with 1 signifying excellent recall and precision.

Coherence

Assesses the logical flow and consistency of generated text.

Perplexity

Measures how well the model predicts a sequence of words or a sample of text. The more consistently the model predicts the correct outcome, the lower its perplexity score.

BLEU (Bilingual Evaluation Understudy)

Assesses the quality of machine-generated text, particularly in translation tasks.

ROUGE (Recall-Oriented Understudy for Gisting Evaluation)

Evaluates the quality of text summaries by comparing them to human-created ones.

Latency

Measures the model’s efficiency and overall speed.

Toxicity

Measures the presence of harmful or offensive content in model outputs.

Applying LLM evaluation frameworks and benchmarks

LLM evaluators establish clear evaluation criteria and then select an evaluation framework that offers a comprehensive methodology for assessing a model’s performance. For example, IBM’s Foundation Model Evaluation framework (FM-eval) is used for validating and evaluating new LLMs in a systematic, reproducible and consistent way.

Within evaluation frameworks are LLM benchmarks, which are standardized datasets or tasks that are used to analyze results and guide the evaluation process. While frameworks define how to evaluate an LLM, benchmarks define what to evaluate—in other words, the specific tasks and data.

LLM benchmarks consist of sample datasets, tasks and prompt templates to test LLMs on specific skills, such as question-answering, machine translation, summarization and sentiment analysis. They also include metrics for evaluating performance and a scoring mechanism. Their assessment criteria can be based in ground truth or human preferences.

By evaluating LLMs on these benchmarks, developers can compare the performance of different models and track progress over time. Some examples of widely used LLM benchmarks include:

  • MMLU (Massive Multitask Language Understanding) dataset, which consists of a collection of multiple-choice questions spanning various domains.
  • HumanEval, which assesses an LLM’s performance in terms of code generation, especially functional correctness.
  • TruthfulQA, which addresses hallucination problems by measuring an LLM’s ability to generate truthful answers to questions.
  • General Language Understanding Evaluation (GLUE), and SuperGLUE, which tests performance of natural language processing (NLP) models, especially those designed for language-understanding tasks.
  • The Hugging Face datasets library, which provides open source access to numerous evaluation datasets.

The selected benchmarks are introduced to the LLM through zero-shot, few-shot and fine-tuning tests to see how well the model operates. With few-shot tests, the LLM is evaluated on its ability to perform with limited data after it receives a small number of labeled examples that demonstrate how to fulfill the task. Zero-shot tests ask the LLM to complete a task without any examples, testing how it adapts to new circumstances. And fine-tuning trains the model on a dataset similar to what the benchmark uses in order to improve the LLM’s command of a specific task.

LLM evaluation results can be used to refine and iterate the model by adjusting parameters, fine-tuning or even retraining on new data.

LLM as a judge vs. humans in the loop

When evaluating model outputs, developers and researchers use two approaches: LLM-as-a-judge and human-in-the-loop evaluation.

In LLM-as-a-judge evaluation, the LLM itself is used to evaluate the quality of its own outputs. For example, this might include comparing text that is generated by a model to a ground-truth data set, or using metrics such as perplexity or F1 to measure results.

For a human-in-the-loop approach, human evaluators gauge the quality of LLM outputs. This type of evaluation can be useful for more nuanced assessments, such as coherence, relevance and user experience, which are difficult to capture through automated metrics alone.

LLM evaluation use cases

LLM evaluation has many practical use cases. Some examples include:

Evaluating the accuracy of a question-answering system

In retrieval-augmented generation (RAG), LLM evaluation can help test the quality of answers that are generated by the model. Researchers can use datasets such as SQuAD (Stanford Question Answering Dataset) or TruthfulQA to check the accuracy of an LLM-powered question-answering system by comparing the model's responses to the ground truth answers.

Assessing the fluency and coherence of generated text

Using metrics such as BLEU and human evaluation, researchers can test the quality of text responses that are offered by chatbots or machine translation systems. This helps ensure that the generated text is fluent, coherent and appropriate for the context.

Detecting bias and toxicity

By using specialized datasets and metrics, researchers can evaluate the presence of biases and toxic content in LLM-generated text. For example, the ToxiGen dataset can be used to assess the toxicity of model outputs, which might lead to safer and more inclusive applications.

Comparing the performance of different LLMs

Researchers can use benchmark datasets such as GLUE or SuperGLUE to compare the performance of different LLMs across various NLP tasks, such as sentiment analysis or named entity recognition.

In these and other uses cases, LLM evaluation can yield important benefits for businesses. By identifying areas for improvement and opportunities to address weaknesses, LLM evaluation can lead to a better user experience, fewer risks and a potential competitive advantage.

Challenges of LLM evaluation

For all its benefits, LLM evaluation also faces some challenges and limitations. The fast pace of LLM development makes it difficult to establish standardized, long-lasting benchmarks. Evaluating contextual understanding is challenging, as is detecting the finer nuances of bias.

Explainability is also an issue: LLMs are often seen as "black boxes," making it difficult to interpret their decision-making process for the purposes of evaluation and to identify the factors that contribute to their outputs.

Also, many evaluation datasets are not representative of various languages or cultures. As a result, models that are tested with these datasets might perform well on specific benchmarks but nonetheless falter in real-world scenarios.

As LLMs and other complex machine-learning applications continue to be developed and applied in new ways, overcoming such challenges to ensure robust evaluation will play an important role in helping evaluators and developers improve LLM effectiveness, safety and ethical use.

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