Evaluating Large Language Models: A Complete Guide

It has become crucial for organizations to ensure the safe, secure, and responsible use of LLMs. Evaluating LLMs involves more than assessing their speed; accuracy, consistency, robustness, and ethical considerations also play an essential role in their responsible development and deployment lifecycle. With LLMs being used in sensitive areas such as healthcare, finance, and education, their evaluation is not just about improving performance—it's also about ensuring they don’t cause harm, misinform, or perpetuate biases.

In this guide, we will explore the process of evaluating LLMs and improving their performance through a detailed, practical approach. We will also look at the types of evaluation, the key metrics that are most commonly used, and the tools available to help ensure LLMs function as intended. Before diving in, let’s gain a better understanding of what LLM evaluation entails.

What are LLM evaluations?

LLM evaluation is the systematic process of assessing the performance of Large Language Models to determine their effectiveness, reliability, and efficiency. LLM system evaluation helps developers understand the model’s strengths and weaknesses, ensuring that it functions as expected in real-world applications. Evaluations also play a vital role in mitigating risks like biased or misleading content, which can damage user trust and lead to adverse consequences. Evaluations can generally be divided into two categories:

• Model evaluation: This assesses the fundamental capabilities of the LLM itself, such as its accuracy, coherence, and ability to generalize. It focuses on evaluating how well the model generates responses without considering its integration into a larger system. This includes assessing the language model’s fluency, understanding, grammar, coherence, and logical consistency.
• System evaluation: This examines how the LLM integrates within a specific application or system, including its responses to user inputs and performance under different operating conditions. It also evaluates the end-user experience and how well the LLM can handle dynamic or diverse scenarios when used in conjunction with other components, such as user interfaces or external databases.

Both types of evaluations are essential for building trustworthy LLM-powered applications. They help developers pinpoint issues that can impact both the model’s core functionalities and its usability in specific contexts.

Why is LLM evaluation important?

The evaluation of LLMs is pivotal to ensuring their responsible use, particularly when used in customer-facing applications. Issues like hallucination, no bias detection, and the inability to respond adequately to certain types of prompts can have serious implications. Here are some of the key reasons why LLM evaluation is essential:

1. Ensuring factual accuracy

One of the significant challenges of using LLMs is their tendency to generate "hallucinations"—convincing but factually incorrect or fabricated information. By evaluating LLMs for factual accuracy, developers can identify the likelihood of hallucinations and take corrective measures. This involves testing LLMs against datasets with known, verifiable answers and assessing the frequency of incorrect responses. Evaluating factual accuracy is particularly crucial in domains where incorrect information could lead to real-world harm, such as medical or legal advice.

2. Mitigating bias

LLMs are trained on vast datasets that may contain inherent biases. Without proper evaluation, LLMs can perpetuate and amplify these biases, leading to unfair or unethical outputs. Evaluating LLMs for fairness and bias helps minimize their impact and ensure inclusive results. Bias evaluation involves testing the LLM against datasets that include demographic diversity to observe how the model responds to different user groups. Bias mitigation not only protects marginalized communities from unfair treatment but also helps build a more equitable AI system.

3. Performance consistency

Evaluating LLMs helps assess a model's performance and its ability to generate consistent results across different tasks and conditions. Consistency in performance is crucial when deploying LLMs for specific applications, especially in domains like healthcare and finance, where accuracy is non-negotiable. Inconsistent responses can lead to confusion or loss of trust in the system. By systematically evaluating consistency, developers can improve the reliability of LLM-powered applications and ensure the output remains stable even with changing inputs or evolving contexts.

4. User satisfaction and relevance

By evaluating LLM outputs and responses based on metrics like relevance and completeness, developers can determine whether the models are providing users with valuable, coherent, and relevant information that meets their needs. User satisfaction is influenced by the LLM's ability to understand context and respond appropriately, particularly in conversational AI. Evaluating user satisfaction requires real-time feedback from users as well as systematic testing with different prompt variations to measure the relevance of LLM outputs.

5. Ensuring ethical use

LLMs can be used to influence opinions, disseminate information, and even spread propaganda. It is therefore essential to evaluate LLMs to ensure they are used ethically and responsibly. Evaluation for ethical use involves testing LLMs for output that could cause harm, such as offensive or misleading information. Incorporating ethical guidelines and assessing model responses against these standards help prevent misuse and foster trust with users.

LLM evaluation metrics

Here’s a list of the most critical evaluation criteria and metrics to consider before launching an LLM application to production. Metrics serve as scoring mechanisms that assess an LLM’s outputs based on given criteria. Effective LLM evaluation includes a mix of automated metrics and human assessments to provide a holistic view of performance.

1. Response completeness and conciseness

This metric determines if the model’s response adequately covers the user's query. Completeness is essential to ensure that the information provided answers the prompt fully, while conciseness measures how succinct and relevant the response is. A complete but verbose response may overwhelm users, whereas a concise yet incomplete response may not serve its purpose. Evaluating completeness and conciseness involves balancing between delivering detailed, informative content and ensuring it remains easy to read and understand.

2. Text similarity metrics

Text similarity metrics, such as cosine similarity, BLEU score, or ROUGE score, are used to compare the generated text to reference or benchmark texts. These metrics help gauge how well a particular LLM can reproduce desired responses or follow specific prompts. High similarity scores indicate that the LLM-generated output is aligned with human expectations. For machine translation tasks, the BLEU score can indicate translation accuracy, while ROUGE is more commonly used for summarization tasks to ensure that generated summaries capture key elements of the original content.

3. Question answering accuracy

Accuracy in question answering is a key performance metric for LLMs. It measures how well the LLM can respond to direct questions and how factually correct the answers are. Benchmark datasets like SQuAD are often used to assess the LLM’s accuracy. Evaluating accuracy for question-answering applications also involves understanding the model’s ability to deal with ambiguous questions and how it manages to generate answers when the prompt lacks context or contains conflicting information. How the model works with retrieval augmented generation is particularly important in this context, as it enhances the model's ability to provide contextually relevant and accurate answers.

4. Relevance

Relevance measures the appropriateness of an LLM’s response to a given prompt. Even if a response is factually correct, it may not be useful or relevant to the user’s specific question. Evaluating relevance helps ensure user satisfaction and boosts the overall quality of interactions. Assessing relevance often involves collecting user feedback and using metrics such as normalized discounted cumulative gain (nDCG) to quantify how well the responses meet user expectations.

5. Hallucination index

The hallucination index identifies how much an LLM is making up information. This metric is particularly crucial for reducing misleading or factually incorrect responses that could harm user trust or lead to incorrect conclusions. Hallucinations can be evaluated by comparing generated responses against trusted datasets or employing domain experts to verify the correctness of the information. Reducing hallucinations ensures that LLMs are safe and reliable to use, particularly for applications in high-stakes industries like healthcare.

6. Toxicity

Toxicity evaluation is critical to ensure that LLM-generated content does not include offensive or harmful language. Toxicity detection tools help flag inappropriate content, ensuring the LLM’s outputs are safe for public use. Evaluating toxicity involves using tools like Perspective API to score generated responses on toxicity, harassment, and hate speech. This metric is particularly important for AI systems deployed in customer support or public forums, where the potential for harmful language could negatively impact user experiences.

7. Task-specific metrics

Depending on the task, specialized metrics such as ROUGE for summarization, METEOR for machine translation, or F1 scores for entity recognition may be used to evaluate specific aspects of the LLM's performance. Task-specific metrics are important for evaluating performance in specialized contexts, providing insights into how well the LLM handles unique challenges associated with particular applications. For example, the F1 score is often used to evaluate models in information extraction tasks to determine how well the model identifies and categorizes entities.

LLM evaluation best practices

Evaluating large language models (LLMs) is a multifaceted process that requires a strategic approach to ensure comprehensive assessment and continuous improvement. By adhering to best practices, developers can enhance the reliability, accuracy, and ethical use of LLMs. Here are some key best practices to consider:

Leveraging LLMOps

LLMOps (Large Language Model Operations) is a crucial aspect of LLM evaluation, enabling the efficient deployment and management of large language models. By leveraging LLMOps, developers can streamline the evaluation process, reduce costs, and improve overall model performance. Here are some best practices for leveraging LLMOps:

• Utilize cloud-based infrastructure: Cloud platforms offer scalable resources that can handle the computational demands of LLMs, making it easier to deploy and evaluate models at scale.
• Implement automated testing and validation pipelines: Automation helps in consistently applying predefined evaluation metrics, ensuring that models are rigorously tested and validated before deployment.

• Monitor model performance: Continuous monitoring allows for real-time insights into model performance, enabling timely adjustments to hyperparameters and other configurations.

• Use containerization: Containerization ensures consistent environments across different stages of the evaluation process, reducing discrepancies and improving reproducibility.

Multiple evaluation metrics

Using multiple evaluation metrics is essential for a comprehensive assessment of LLMs. This approach allows developers to evaluate models from different perspectives, identifying strengths and weaknesses that might not be apparent through a single metric. Some popular evaluation metrics for LLMs include:

• Perplexity: Measures the model’s ability to predict the next word in a sequence, providing insights into its language understanding capabilities.
• BLEU Score: Evaluates the quality of machine translation by comparing generated translations to reference translations.
• ROUGE Score: Assesses the quality of text summarization by measuring the overlap between generated summaries and reference summaries.
• METEOR Score: Another metric for evaluating machine translation, focusing on precision, recall, and alignment.

Real-world evaluation

Real-world evaluation is critical for ensuring that LLMs perform well in practical applications. This involves testing the model on real-world data, simulating real-world scenarios, and gathering feedback from human evaluators. Best practices for real-world evaluation include:

• Use diverse and representative datasets: Ensure that the evaluation datasets reflect the diversity and complexity of real-world data.
• Simulate real-world scenarios: Test the model in scenarios that mimic actual use cases, including edge cases and rare events.
• Gather feedback from human evaluators: Human feedback provides valuable insights into the model’s performance and user satisfaction.
• Continuously monitor and update the model: Regularly update the model based on real-world performance data to address emerging issues and improve accuracy.

Context-specific evaluation

Context-specific evaluation is essential for ensuring that LLMs perform well in specific contexts and domains. This involves tailoring the evaluation process to the specific use case, taking into account factors such as domain knowledge, terminology, and cultural nuances. Best practices for context-specific evaluation include:

• Use domain-specific datasets and metrics: Employ datasets and evaluation metrics that are relevant to the specific domain or application.
• Incorporate domain experts: Engage domain experts in the evaluation process to provide insights and validate the model’s outputs.
• Account for cultural and linguistic nuances: Ensure that the model is sensitive to cultural and linguistic differences, particularly in multilingual applications.
• Continuously monitor and update the model: Regularly review and update the model to maintain its relevance and accuracy in the specific context.

LLM evaluation frameworks and tools

LLM evaluation frameworks and tools provide standardized benchmarks for measuring and improving the performance, reliability, and fairness of language models. Both automated metrics and human- evaluation tools are often combined to achieve a comprehensive understanding of LLM performance. Below are some key tools and frameworks that are widely used in the evaluation of LLMs:

1. DeepEval

DeepEval is an open-source evaluation framework that helps organizations track important LLM performance evaluation metrics, including contextual recall, answer relevance, and faithfulness. It is useful for understanding how the model performs on specific tasks and ensuring it adheres to quality standards. DeepEval can be particularly effective for evaluating both the quality of language generation and the appropriateness of outputs in sensitive contexts.

2. promptfoo

Promptfoo is a command-line interface (CLI) and library for systematically evaluating LLM prompts and performance. With promptfoo, developers can test different prompts and measure the response quality. It’s particularly useful for prompt engineering and optimizing LLM outputs. The CLI allows for quick, reproducible experiments that can help refine model behavior through iterative adjustments of prompt phrasing.

3. EleutherAI LM Eval

EleutherAI LM Eval is a benchmarking tool that supports few-shot evaluation across a wide variety of tasks. It enables researchers to measure the performance of LLMs on tasks ranging from both natural language processing and inference to reading comprehension. This tool is invaluable for assessing the generalization capabilities of LLMs, ensuring that they perform well not just on training data but also on new, unseen tasks with minimal fine-tuning.

4. MMLU

Massive Multitask Language Understanding (MMLU) is an LLM evaluation framework designed to test language models on a broad range of subjects using zero-shot and one-shot learning settings. MMLU is often used to benchmark the general knowledge and adaptability of LLMs. By testing LLMs across different domains, MMLU helps identify areas where the model excels and where improvements are needed, giving a more holistic view of its versatility.

5. BLEU (BiLingual Evaluation Understudy)

BLEU is a common metric used for machine translation tasks. It measures the semantic similarity of machine-generated translations to high-quality reference translations on a scale from 0 to 1. A higher BLEU score indicates better translation accuracy and fluency. BLEU is widely used in language generation tasks to ensure that the model's outputs match the linguistic quality and structure of benchmark translations, thus providing reliable translations.

6. SQuAD (Stanford Question Answering Dataset)

SQuAD is a dataset used to evaluate LLMs for question-answering tasks. It includes context passages with corresponding questions, and LLMs are evaluated based on their ability to generate the correct answers based on the given context. SQuAD evaluations provide a robust measure of an LLM's comprehension and ability to provide concise, accurate responses in a question-answer format. It is considered a gold standard for assessing the precision of question-answering systems.

7. OpenAI Evals

OpenAI Evals is a comprehensive evaluation framework that provides benchmarks for assessing LLM model outputs. It enables developers to test model accuracy, coherence, and reliability using a standardized methodology. OpenAI Evals also incorporates a wide range of test cases that address both common and edge scenarios, making it particularly valuable for fine-tuning LLMs to ensure that they function well in real-world applications.

8. UpTrain

UpTrain is an open-source evaluation tool that provides pre-built metrics to check LLM responses, including correctness, hallucination, and toxicity. It also includes tools for generating synthetic data and supporting LLM-assisted automated evaluations, making it ideal for assessing LLMs in various environments. UpTrain provides real-time monitoring and dashboards that help track model performance over time, enabling proactive identification of areas where improvements are needed.

9. H2O LLM EvalGPT

H2O LLM EvalGPT is an open tool for benchmarking LLM performance across a wide array of tasks and datasets. It is suitable for gaining a holistic view of model strengths and areas needing improvement. The tool enables a thorough performance assessment of of LLMs using a variety of task-based benchmarks, providing insights into how well the model adapts to different types of questions, tasks, or user scenarios.

Evaluating LLMs with UpTrain: Notebook tutorial

If you haven’t already, sign up for your free SingleStore trial to follow along with the tutorial. We will be using SingleStore Notebooks, which are just like Jupyter Notebooks but with the additional capabilities and benefits of an integrated database.

When you sign up, you need to create a workspace.

Go to the main dashboard and click on the Develop tab.

Create a new Notebook, and name it whatever you’d like. 

Now you can get started. Add all the code shown here in the notebook you created. Create a database named ‘evaluate_llm’.

Install the necessary packages

The next step involves setting the required environment variables — mainly the openai key (for generating responses), singlestoredb (for context retrieval) and uptrain api key (for evaluating responses). You can create an account with UpTrain and generate the API key for free.

Add the UpTrain API key.

Import necessary modules

Load data from the web

Next, split the data

Set up the SingleStore database with OpenAI embeddings

The complete step-by-step Notebook code is present here in our spaces.

Finally you will run evaluations using UpTrain, an open-source LLM evaluation tool. You will be able to access the UpTrain dashboards to see the evaluation results. We can experiment with different chunk sizes to see the varied outcomes.

UpTrain's API client also provides an evaluate_experiments method which takes the input data and evaluates it along with the list of checks to be run, and the name of the columns associated with the experiment.

By following the LLM evaluation approach and tools as shown in the tutorial, we gain a deeper understanding of LLM strengths and weaknesses. This allows us to leverage their capabilities responsibly — mitigating potential risks associated with factual inaccuracies and biases. Ultimately, effective LLM evaluation paves the way for building trust and fostering the ethical development of AI in various LLM-powered applications.

Try our Evaluating LLMs with UpTrain Notebook today!