LLM Evaluation: Key Metrics, Methods, Challenges, and Best Practices

Have you found yourself immersed while talking with a chatbot? Do you know why? It’s because of the power of LLM (Large Language Model). It is a type of AI system that can understand and generate human-like responses by processing them from a massive database.

LLMs are not just meant for general responses but also for tasks such as email writing, programming code, health diagnosis, and more. Indeed, many industries have adopted LLM to streamline their operations and offer better user experiences.

But we have just looked at one side of the coin. On the other hand, these LLM-powered chatbots often commit minor errors, which may lead to unusual troubles. So, whether you are an AI researcher, developer, data scientist, or business owner, working on AI chatbot development is not enough; you also need to consider LLM evaluation to ensure their reliability, accuracy, and performance. 

In this guide, we will discuss LLM evaluation essentials, metrics, frameworks, challenges, and best practices.

So, let’s get the ball rolling.

What is LLM Evaluation?

LLM evaluation is the streamlined process of testing & measuring the effectiveness and performance of the models in real-world scenarios. These models understand the user’s queries and respond effectively to tasks like text generation, video summarization, translation, and question answering.

The developers extensively test these models to determine what’s working and what needs improvement. This helps you eliminate the risks of biased or misleading content and lets you work according to the future of LLM model development.

Let’s understand LLM evaluations with an example.

Think about an LLM-based customer support chatbot. The developers need to evaluate its accuracy and contextuality. When a user asks the chatbot, “What are the working hours of this store?” the chatbot should respond efficiently. On the other hand, if the chatbot fumbles to answer queries or give false information, this means it needs improvement. This extensive assessment helps you to refine things accordingly.

| Also Read: Best AI Chatbots

LLM Model Evaluation vs System Evaluations

There are two common types of evaluation:

• Model Evaluation: It accesses the core features and functionalities.
• System Evaluation: It checks how well the system functions with a particular program or user input.    

Knowing the difference between LLM model evaluation and system evaluation is necessary for businesses or developers who want to maximize the benefits of large language models. Let’s examine the differences based on different criteria.

What are LLM Evaluation Metrics?

LLM evaluation metrics are quantitative measures used to check and compare the performance and overall capabilities of large language models across a range of dimensions. These include language understanding, text generation quality, language translation, writing different types of content, and responding to queries. 

All the metrics offer several sets of rules for evaluating the LLM models. Thus, they give developers and researchers a general idea of model enhancements, allow them to benchmark against other models, and determine the areas for enhancement.

Here are some of the basic metrics or criteria that businesses and developers can utilize to measure the performance of the LLM models before launch.

• Response Completeness and Conciseness: It identifies whether the generated response addresses the user’s query fully or not. Conciseness judges the relevancy of the response generated.
• Text similarity metrics: This metric compares the output with a reference or a benchmark text, measuring their similarity. A score is allocated depending on how LLM is responding.
• Question Answering Accuracy: This measure determines whether the LLM response resolves the query in a simple, concise, and effective manner.
• Hallucination index: This index determines the extent to which the LLM has offered fake or made-up information. It even showcases LLM providing a partial output for the entered query.
• Toxicity: It evaluates the percentage of racist, biased, or toxic comments in the LLM’s response.
• Task-Specific metrics: This means using different metrics for tasks such as text summarization, translation, etc. 

Most Popular LLM Evaluation Methods

The evaluation methods or metrics are further classified into different types. Let’s look at each of them in detail.

Automated Evaluation Metrics

Here are some of the most well-known automated evaluation metrics for LLM.

• Perplexity

Perplexity is mostly utilized to check the performance of large language models (LLMs). It indicates to what extent the LLM predicts the sample of text and showcases the overall uncertainty of the LLM’s predictions. Here, the lower the number, the better the model is known for providing great output. 

The formula used to calculate the score of the model is as follows.

Perplexity =exp(−1/N∗sum(log(P(wi∣w1,w2,…,wi−1))))

In this, N is referred to as the number of words in the sequence and P(wi​∣w1​,w2​,…,wi−1​) is basically the probability the word wi​ gives to the preceding words w1,w2,…,wi−1w1​,w2​,…,wi−1​. Lastly, the log is referred to as a natural algorithm.

Perplexity is even beneficial at the time of model training and fine-tuning because it offers various signs of model enhancements. 

Even though the perplexity score is useful, it might not correlate with the human-based understanding and attributes while generating content.

• BLEU Score

BLEU stands for Bilingual Evaluation Understudy. This metric is handy for checking the overall worth of the machine-generated text. It compares n-grams(sequence of words) in translated text with one or more reference translations. 

The BLEU score depends heavily on precision. It analyzes the total number of times n-grams of generated text are present in the reference translations. It even implements a brevity penalty for shorter text.

The score ranges from 0 to 1, with a higher score indicating that the provided translation is similar to the human-generated text, showcasing enhanced fluency and competence.

Even though the metric is excellent when it comes to translation tasks, the model lacks when we consider checking creative or highly differentiative outputs.

• F1 Score

F1 Score combines precision and recall into one metric and is known for maintaining a balance between them. These metrics can be leveraged for classification and question-answering tasks because they give a complete picture of the specific model’s performance.

Precision calculates the authenticity of the optimistic predictions, which showcases the total number of positive predictions are really the true positives.

Precision = (Total matching tokens in generated text) / (Total tokens in generated text)

The recall is particularly the model’s ability to determine the appropriate instances, which presents the overall number of real positives that are actually predicted.

The F1 Score ensures a perfect balance between precision and recall.

F1 Score = 2 (Precision)*Recall/ Precision+Recall

If this score ranges between 0 to 1, it means the model is robust and accurate.

• METEOR

Meteor stands for Metric for Evaluation of Translation with Explicit Ordering and is specially built to address the issues present in BLEU. This metric is handy for checking the quality of machine translations. The metrics match the machine-generated text with the reference text by taking into account synonyms, paraphrases, and stemming. 

Here, meteor checks the performance of the machine-generated text by examining the alignment between the generated and referenced text, considering the meaning instead of just its raw form. The score is calculated with a harmonic mean of unigram precision and recall, with recall given higher importance than precision.

The meteor score ranges from 0 to 1; the higher the score, the better the translations. The metrics work well for contexts where natural language diversity is needed, thus improving the evaluation precision.

• BERTScore

BERTScore analyzes the text quality by matching the BERT’s deep contextual embeddings with the reference text by using cosine similarity. 

Rather than exactly matching the words, the metric estimates word similarity by considering their meaning and context. Hence, the model is great for tasks involving machine translation and summarization.

The metric makes the most of a greedy matching algorithm to determine the optimal word alignments. It also calculates precision, recall, and F1 measures depending on their similarity.

Formula: BERTScore = F1(R, C)

Here, R is known as the reference text, C is referred to as the candidate text, and F1 is determined using BERT-based word similarities.

• ROUGE Score

ROGUE (Recall-Oriented Understudy for Gisting Evaluation) is a metric mainly leveraged for the evaluation of summaries. It utilizes n-grams, sequences, and word pairs to determine how much of the generated content from the model overlaps with the content present in the reference text. 

ROUGE is excellent for analyzing automatic text summarization and machine translation.

In general, the metric is dependent on recall to define the total number of n-grams in the text. It even offers some key variations for extensively evaluating the summarization for content relevance and fluency.

Human Evaluation Metrics

As the name suggests, human evaluation relies on humans’ judgment instead of machines’. While automated evaluations are speedy, they lack the nuances humans can only understand.

In human evaluation, real people invest a lot of time and effort into checking the effectiveness and appropriateness of generated output. This metric allows you to look after many subjective factors, such as language fluency, coherence, contextual understanding, factual accuracy, relevance, and meaningfulness. 

Different types of methods are used for human evaluation. They are as follows:

• Likert Scale: In this metric, humans rate the generated output based on a set of criteria, such as coherence, relevance, fluency, etc. They can rate an output 1(completely irrelevant) and 5 ((perfectly fluent).  The method makes quantitative analysis simpler and also makes comparisons easier in different samples. The only drawback is that metrics might introduce biases where the evaluators might be unable to give extreme feedback.
• Preference Judgements: In this method, humans are presented with two or more generated outputs. The technique emphasizes quality and allows users to choose the output that aligns with their needs or expectations. The major drawback of this model is that when selecting an answer between the two, humans need to pay more attention to essential details and reasons for choosing one option over the other.

• Fine-grained: This method comprises an in-depth analysis or offering feedback through comments. Here, the human checks the grammar, syntax, coherence, and relevance of the output. They can even check different elements, such as the facts, word choice, punctuation, readability, etc. The only limitation of this method is that the evaluator should know everything before offering any fine-grained feedback. 
• A/B Testing: In this metric, the users have the power to test the model in different real-world scenarios and then obtain feedback. Based on this, they can improve their model and offer a better user experience.

Task-Based Metrics

Task-based metrics do an evaluation of LLM models by considering their performance on specific downstream tasks and benchmarks. These metrics provide in-depth insights into how each model performs in real life.

• Downstream Task Performance: It evaluates how the model performs in a particular set of tasks, such as question answering, summarization, machine translation, code generation, and more.  By accessing the overall performance of the LLM model in these tasks, we can identify the reliability and utility in the real world.
• Benchmarks: Benchmarks consist of pre-defined datasets, and evaluation gauges are often run on the models to assess their performance on particular tasks. As this method has a framework, you obtain consistent feedback on the good and bad aspects of different models and approaches.

Ethical & Safety Evaluations

With time, various businesses and organizations worldwide will start using LLMs daily. Therefore, it is crucial to ensure that these models follow ethical and safety evaluations to operate at their best and even comply with societal values.

• Bias Detection

Take a thorough look at LLM outputs for unfair treatment or illustration of different groups. The metric identifies and assesses how well the model neglects any stereotypes associated with age, race, gender, religion, etc.

• Toxicity Measurement

This metric checks the overall possibility of any harmful, inappropriate, or offensive content. It even leverages some algorithms to classify and score output depending on their potential to increase hate speech, profanity, or similar kinds of content.

• Factual Correctness Checks

It determines the overall authenticity of the information generated by LLM chatbots. It compares the outputs with reliable sources or trusted facts to be entirely certain that the information offered is trustworthy, especially when misleading or false information can have a severe impact.

• Privacy and Security Assessment

This metric evaluates and ensures that the LLM model doesn’t accidentally disclose any sensitive information. It also closely monitors how the model handles private data, offers protection against leaks, and follows the latest privacy regulations of the region.

Cross-Domain Evaluation

Cross-domain evaluation checks the way a large language model performs across a range of subject topics or tasks. This metric helps to determine a model’s adaptability and generalization ability to ensure that it doesn’t generate output only for one area.

The procedure comprises testing the LLM for various domains, such as science, technical domains, literature, current events, creative writing, and more. By doing thorough performance testing of the model across these domains, developers can know all the pros and cons, making sure it fulfills users’ needs and blends well with different applications. It even helps in building highly potent, next-gen, and user-friendly large language models.

Using LLM as a Judge

It is also known as AI evaluating another AI. It is one of the most robust approaches in LLM evaluation. This approach allows you to leverage one AI model to make rapid evaluations across vast datasets to determine the complex patterns or errors humans often miss. 

For instance, ChatGPT-4 can be leveraged to access the coherence and relevance of responses generated by any other AI. This approach can analyze the text and offer valuable insights regarding quality. It works well for tasks requiring a detailed analysis that is impossible via traditional metrics or humans.

However, there is a catch: relying entirely on AI is also not good. Why? AI generates output based on biases, preferring specific types of responses while missing the subtle nuances that humans can quickly identify. It further leads to the “echo chamber” effect, where you generally obtain answers that are pre-defined, sidelining creativity.

Besides this, AI lacks explaining the output. It might grade the answers but offer a detailed analysis that a human would. It is similar to getting a score without knowing the reason behind it.

The best way to handle this situation is by following a hybrid model. Consider combining AI’s capability with human analyses for a balanced evaluation. Here, AI offers data in bulk and manages it, while humans focus more on content and depth, leading to an extensive understanding of an LLM.

11 Best LLM Evaluation Frameworks and Tools

As the world of AI constantly evolves, various new LLM frameworks and tools have been released into the industry. These new-age LLM frameworks and tools come with the best features and functionalities, such as model training and monitoring of performance, reliability, and fairness. 

With so many LLM evaluation frameworks in the market, it becomes difficult to choose the right one. Here, we have shortlisted the most popular LLM evaluation frameworks in no particular order.

1. Hugging Face

Hugging Face comes with pre-trained models, databases, and building blocks required for building NLP applications. Consider it a GitHub for machine learning. Why? The framework has an extensive library and other community-contributed resources that resonate well with developers and researchers. 

It has an intuitive and modern interface that they can use to find, share, collaborate, and experiment with the LLM models.

2. Azure AI Studio

Microsoft’s Azure AI Studio comes with some of the best enterprise-grade tools, built-in metrics, and customizable evaluation flows. The framework offers an extensive environment suitable for building, deploying, evaluating, and managing Gen AI applications. It is best suited for anyone working with the Microsoft ecosystem and wants a framework that delivers enhanced security and performance.

3. DeepEval

DeepEval is one of the most popular LLM evaluation tools, and it is designed especially for evaluating LLM applications and integrating effortlessly with current ML pipelines. The framework heavily emphasizes on various metrics such as contextual recall, answer relevance, and faithfulness, along with the set benchmarks to evaluate the performance of models and applications in a go. 

This evaluation framework helps you discover what’s working and what needs improvement to ensure your LLM meets real-world application requirements. It is like having a team of quality assurance agents at your disposal.

4. PromptFlow

PromptFlow is a well-known framework for the evaluation of LLM models. The tool is specially built to facilitate the entire development process, from ideation, prototyping, testing, evaluation, and optimization of LLM applications and models. It has a highly intuitive interface for building complete prompt chains and testing multiple variations. 

Consider it a digital playground for prompt engineering, where you can constantly improve the output based on analytics to develop apps that are ready for launch. Further, you can continuously evaluate and fine-tune LLM models.

5. Langsmith

Langsmith is a robust LLM evaluation framework developed and launched by Anthropic. It is even considered a complete developer platform as it helps developers with every step of LLM application development. This includes planning, development, debugging, collaborating, testing, and monitoring LLM applications. The tool is suitable for the development of language-centric AI applications. 

The framework offers several built-in tools for handling and enhancing LLMs. It allows users to leverage NLP techniques to analyze and improve the models to align well with the target audience’s needs.

6. TruLens

TruLens is a popular open-source package that prioritizes transparency and interpretability in LLM evaluations. It is an excellent tool for developers and stakeholders to understand how LLMs reached this conclusion, thus increasing trust and compliance with privacy policies.

The tool evaluates the quality of the LLM-based applications considering multiple feedback functions, such as groundedness, context relevance, safety, etc. Feedback functions are internally programmed to check the quality of inputs, outputs, and instant results. The tool works well for use cases such as question answering, summarization, retrieval-augmented generation, and agent-based apps.

7. Vertex AI Studio

Vertex AI Studio is a cloud-based console tool that works well when building, training, and deploying AI models along with the LLMs. The tool allows you to test sample prompts, develop your own prompts, and modify the foundational models to manage tasks that align with the application’s needs. It even allows you to connect LLM models with third-party services. 

Moreover, the tool doesn’t just offer support till deployment; it also allows users to scale, manage, and monitor the foundational models with the help of Vertex AI’s end-to-end MLOps capabilities and an entirely managed API infrastructure.

8. TensorFlow

TensorFlow is an open-source machine learning LLM evaluation framework. The library was developed by Google’s internal research and production team. It comprises highly adaptable tools, libraries, and community resources necessary for evaluating LLM models. These resources are also extensively leveraged for research breakthroughs and building production-ready applications.

9. Parea AI

Parea AI is a developer platform that enables AI engineers to develop production-ready LLM applications. The evaluation framework has a modern user interface and offers necessary tools for training models through prompts, managing these prompts into datasets, and evaluating the performance of LLM applications. Simply put, the framework enables debugging, monitoring, and optimizing LLM applications in the best way possible through these tools.

10. Weights & Biases

Weights & Biases is an AI development platform made especially for the GenAI industry. This tool streamlines the entire process of LLM evaluations by offering extensive visualization and tracking tools. With this tool, developers and researchers can train their foundational models, fine-tune any known or known model, manage products from experimentation to training to production, or create robust AI applications backed up by LLMs.

The platform offers three major components: training, fine-tuning, and leveraging the foundation models. W&B models for training and fine-tuning models, W&B Weave for tracking & evaluating LLM applications, and W&B Core offers necessary building blocks for tracking and visualizing data and models and communicating results.

11. Amazon Bedrock

Amazon Bedrock streamlines the entire process of evaluation and deployment for the large language models. It is a fully managed service that gives you access to the most popular foundational models from learning companies worldwide for developing generative AI applications, and it is backed up by AWS’s solid infrastructure.

You can evaluate and choose the most suitable foundational model for your Gen AI application. Amazon Beckrock gives you tools to craft generative AI apps with high security, privacy, and responsiveness. 

The best thing about Amazon Bedrock is that it is model-independent and leverages just a single API. Hence, it allows you to consider different foundational models and even upgrade to the most recent versions without any significant code changes. It also allows you to fine-tune existing models or integrate any custom models.

What are the Challenges in LLM Evaluation?

Here is the list of common challenges that businesses and developers face while evaluating an LLM.

1. Subjectivity in Human Evaluation

Human evaluation is one of the prominent methods for evaluating LLMs. However, this method brings many biases and interpretations to the table. Not all evaluators give the same importance to fluency, coherence, and relevance. Hence, what one user finds perfect, another user might find poor. 

In addition, different users will have diverse opinions about the same output because their evaluation criteria are not up-to-the-mark.

Besides this, this method is pricey and time-consuming when it comes to large-scale evaluations. 

2. Biases & Fairness in Automated Evaluations

Automatic evaluation metrics for LLM usually continue with the biases already present in their data. These biases can be of different types and affect the output of the LLM. Let’s have a quick look at a few of them. 

Order Bias: Preferring first or last items in a sequence.

Gender Bias: It is generating responses by favoring one gender over the other. For instance, linking certain professionals with a particular gender. 

Age Bias: Making unfair decisions considering the younger generations or older people.

Ego Bias: Evaluators might consider outputs that match well with your self-beliefs and experience instead of a thorough evaluation process.

Attention Bias: Giving importance to specific outputs while neglecting others, thus leading to unfair results.

3. Adversarial Attacks

LLMs can be tricked into a certain behavior by specific types of inputs. Hence, they are prone to adversarial attacks, such as model manipulation or data position. These attacks hinder their understanding, leading to biased, false, or harmful outcomes. 

The popular evaluation methods still lack features and functionalities to detect these attacks. A thorough evaluation is still in the research phase. 

In addition, high-end gen AI models might suffer from legal or ethical issues down the line, which ultimately affects the LLMs used in particular businesses.

4. Scalability Issues 

With time, LLMs have the capacity to tackle a complex and diverse range of tasks. To handle these tasks, LLMs continuously require extensive reference data and collecting this extensive data is challenging. It is similar to writing an essay on a topic you know nothing about. Moreover, inadequate data affect evaluation and lead to limited analysis of the model’s performance.

5. Lack of Diversity Metrics

Existing evaluation frameworks usually fall short when it comes to LLMs’ performance across different demographics, cultures, and languages. These LLMs prioritize accuracy and relevance when generating output and neglect other essential factors, such as novelty and diversity. Hence, it is hard to say that models perform at their best.

6. Data Quality Issues

The famous quote “Garbage in, Garbage out” is valid here. Poor-quality training or evaluation data in the LLM can cause unreliable outputs or misleading evaluations. Therefore, developers should consider implementing some data curation practices.

Best Practices for Evaluation of LLM Models

Evaluating LLM models doesn’t just include checking the numbers. It is a combination of art and science. Let us look at some popular strategies that can take your LLM evaluation from good to great. 

1. Setting Clear Objectives

Make thorough goals for LLM evaluations. What do you want to achieve from your LLM? Factual accuracy, instant response, situation analysis, creative genius, etc. Defining the goal helps you to apply efforts in that direction and measure your performance regularly.

2. Balancing Quantitative and Qualitative Analyses

It’s important to not only rely on one thing. Combining numerical data with human evaluation helps you to obtain a full picture of the LLM’s performance. Quantitative analysis provides you with the numerical data. While, qualitative analyses offer nuances from human judgements which numbers can’t reveal.

3. Keep in Mind Your Potential Audience

Define who will use your LLM model’s output. Customize the evaluation criteria according to the needs and characteristics of your targeted audience. This will showcase that your model is relevant and satisfies the needs of the right users.

4. Access Real-World Analysis

Testing out your LLM model in real-world scenarios helps you determine its overall effectiveness, user satisfaction, and flexibility to handle unforeseen situations. For instance, consider implementing either domain-specific or industry-specific metrics to obtain the complete performance insights of the LLMs. This offers valuable insights into how LLM performs, and thus, you can make necessary adjustments.

5. Utilizing LLMops

Utilize the LLM operations (LLMops), a specialized branch of MLOps that simplifies your entire development and improvements of LLMs. It offers some of the best tools and processes that ease your tasks of constant evaluation, performance monitoring, and recurrent enhancement. This process lets you keep your model up-to-date with the growing requirements.

6. Diverse Reference Data

Leverage extensive reference datasets that include multiple perspectives, styles, topics, and contexts. Consider adding examples from a wide range of domains, languages, and 

cultural preferences. This kind of comprehensive data helps you to obtain an in-depth evaluation of your LLM’s abilities and detect any kind of biases in their performance.

Transform Your AI Journey with Strategic LLM Evaluation

Evaluation of LLM models is the most crucial thing if you want to take the maximum benefit out of it and offer an enhanced user experience. By utilizing the most suitable LLM evaluation metrics and adjusting the frameworks as needed, you can resolve any of the current issues in your LLMs and even develop more reliable, secure, and next-gen AI applications. However, don’t neglect the challenges, as they will help you refine your approach to get the most desirable outcomes.

Want to get the most out of your LLM but don’t know where to start? Contact us today. OpenXcell offers best-in-class LLM fine-tuning services to refine your models effectively. Whether you want to modify the LLM model or want to build a next-gen AI model using LLM, we are there to provide you with a suitable solution.