Ultimate Guide to LLM Evaluation Metrics for AI Optimization

As large language models (LLMs) grow in size and complexity, so do the challenges of building and deploying them. For example, evaluating these powerful tools can be challenging. There are many different LLM evaluation metrics, and selecting the right ones can feel overwhelming. Different metrics reveal different insights about model performance, and some may align more closely with your goals than others. This blog will break down multimodal LLM evaluation metrics so you can confidently select and apply the right ones for your project.

Lamatic’s generative AI tech stack offers valuable tools to help you with this task. Our solution will help you assess your LLM against your chosen metrics, ensuring optimal performance and alignment with your generative AI product goals. 

What is LLM evaluation and Its Importance

LLM evaluation tests how well large language models perform in real-world situations. When we test these models, we look at:

• How well they understand and respond to questions
• How smoothly they generate text
• Whether their responses make sense in context

This step is crucial because it helps us catch issues and improve the model, ensuring it can handle tasks effectively and reliably before they go live. 

Why Do You Need to Evaluate an LLM? 

Evaluating an LLM ensures it:

• Understands and responds accurately
• Handles different types of information correctly
• Communicates:
  • Safely
  • Clearly
  • Effectively

This step is essential because it allows us to fine-tune the model based on real feedback, improving its performance and reliability. By doing thorough evaluations, we ensure the LLM can meet the needs of its users, whether it's answering questions, providing recommendations, or creating content. 

Example Use Case in Customer Support 

Let's say you're using an LLM in customer support for an online retail store. Here's how you might evaluate it: 

• You'd start by setting up the LLM to answer common customer inquiries like order status, product details, and return policies. 
• You'd run simulations using a variety of real customer questions to see how the LLM handles them. 

For example, you might ask, "What's the return policy for an opened item?" or "Can I change the delivery address after placing an order?" During the evaluation, you'd check if the LLM's responses are accurate, clear, and helpful. 

• Does it fully understand the questions? 
• Does it provide complete and correct information? 
• If a customer asks something complex or ambiguous, does the LLM ask clarifying questions or jump to conclusions? 
• Does it produce toxic or harmful responses?

You're also building a valuable dataset as you collect data from these simulations. You can then use this data for LLM fine-tuning and RLHF to improve the model's performance. This cycle of constantly testing, gathering data, and improving helps the model work better. It ensures the model can reliably help real customers, improving their experience and making things more efficient. 

Importance of Custom LLM Evaluations 

Custom evaluations are key because they ensure models match customers' needs. You start by:

• Figuring out the industry's unique challenges and goals. 
• Create test scenarios that mirror the real tasks the model will face, such as:
  • Answering customer service questions
  • Analyzing data
  • Writing content that strikes the right chord.

You must also ensure your models can responsibly handle sensitive topics like toxicity and harmful content. This is crucial for keeping interactions safe and positive. This approach doesn't just check if a model works well overall; it checks if it works well for its specific job in a real business setting. This is how you ensure your models help customers reach their goals. 

LLM Model Evals vs. LLM System Evals 

When we talk about evaluating large language models, it's important to understand there's a difference between looking at a standalone LLM and checking the performance of a whole system that uses an LLM. Modern LLMs are pretty strong, handling a variety of tasks like:

• Chatbots
• Recognizing named entities (NER)
• Generating text
• Summarizing
• Answering questions
• Analyzing sentiments
• Translating
• And more

These models are often tested against standard benchmarks like:

• GLUE
• SuperGLUE
• HellaSwag
• TruthfulQA
• MMLU

Using well-known metrics. These LLMs may not fit your specific needs straight out of the box. Sometimes, we must fine-tune the LLM with a unique dataset for our particular application. Evaluating these adjusted models or models that use techniques like Retrieval augmented generation (RAG) usually means comparing them to a known, accurate dataset to see how they perform. But ensuring that an LLM works as expected isn't just about the model itself but also how we set things up. This includes choosing the right prompt templates, setting up efficient data retrieval systems, and tweaking the model architecture if necessary. Although picking the right components and evaluating the entire system can be complex, ensuring the LLM delivers the desired results is crucial.

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• How to Train Your Own LLM

Common LLM Evaluation Metrics and Evaluation Methodologies

Evaluating large language models requires a comprehensive approach, employing various measures to assess their performance. We explore key evaluation criteria for LLMs, including:

• Accuracy and performance
• Bias and fairness
• Other important metrics

Accurately measuring performance is an important step toward understanding an LLM's capabilities. This section dives into the primary metrics utilized for evaluating accuracy and performance.

Perplexity

Perplexity is a fundamental metric for evaluating and measuring an LLM's ability to predict the next word in a sequence. This is how we can calculate it:

• Probability: First, the model calculates the probability of each word that could come next in a sentence.
• Inverse Probability: We take the opposite of this probability. For example, if a word has a high probability (meaning the model thinks it’s likely), its inverse probability will be lower.
• Normalization: We then average this inverse probability over all the words in the test set (the text we are testing the model on).

Lower perplexity scores indicate that the model predicts the next word more accurately, reflecting better performance. It quantifies how well a probability distribution or predictive model predicts a sample. For LLMs, a lower perplexity means the model is more confident in its word predictions, leading to more coherent and contextually appropriate text generation.

Accuracy

Accuracy is a widely used metric for classification tasks, representing the proportion of correct predictions made by the model. While this is a typically intuitive metric, it can often be misleading in the context of open-ended generation tasks.When generating creative or contextually nuanced text, the "correctness" of the output is not as straightforward to define as it is for tasks like sentiment analysis or topic classification. While accuracy is useful for specific tasks, it should be complemented with other metrics when evaluating LLMs.

BLEU/ROUGE Scores

BLEU (Bilingual Evaluation Understudy) and ROUGE (Recall-Oriented Understudy for Gisting Evaluation) scores are used to evaluate the quality of generated text by comparing it to reference texts.

• Bleu Is All About Precision: if a machine translation uses the exact same words as a human translation, it gets a high BLEU score. For example, if the human reference is "The cat is on the mat," and the machine output is "The cat sits on the mat," the BLEU score would be high because many words overlap.
• Rouge Focuses On Recall: it checks if the machine-generated text captures all the important ideas from the human reference. Let's say a human-written summary is "The study found that people who exercise regularly tend to have lower blood pressure." If the AI-generated summary is "Exercise linked to lower blood pressure," ROUGE would give it a high score because it captures the main point even though the wording is different.

These metrics are beneficial for tasks like:

• Machine translation
• Summarization
• Text generation

They provide a quantitative assessment of how closely the model's output aligns with human-generated reference texts.

Bias and Fairness Metrics

Ensuring fairness and reducing bias in LLMs is essential for equitable applications. Here, we cover key metrics for evaluating bias and fairness in LLMs.

Demographic Parity

Demographic parity indicates whether a model's performance is consistent across different demographic groups. It evaluates the proportion of positive outcomes across groups defined by attributes such as race, gender, or age.Achieving demographic parity means the model's predictions are not biased toward any particular group, ensuring fairness and equity in its applications.

Equal Opportunity

Equal opportunity focuses on whether the model's errors are evenly distributed across different demographic groups. It assesses the false negative rates for each group, validating that the model does not disproportionately fail for certain demographics.This metric is crucial for applications where fairness and equal access are essential, such as hiring algorithms or loan approval processes.

Counterfactual Fairness

Counterfactual fairness evaluates whether a model's predictions would change if certain sensitive attributes differed. This involves generating counterfactual examples where the sensitive attribute (e.g., gender or race) is altered while keeping other features constant.If the model's prediction changes based on this alteration, it indicates a bias related to the sensitive attribute. Counterfactual fairness is vital for identifying and mitigating biases that may not be apparent through other metrics.

Other Metrics

Beyond performance and fairness, additional criteria are useful for comprehensively evaluating LLMs. This section highlights these aspects.

Fluency

Fluency assesses the naturalness and grammatical correctness of the generated text. A fluent LLM produces outputs that are easy to read and understand, mimicking the flow of human language.This can be evaluated through automated tools or human judgment, focusing on grammar, syntax, and overall readability.

Coherence

Coherence helps analyze the logical flow and consistency of the generated text. A coherent text maintains a clear structure and logical progression of ideas, making it straightforward for readers to follow. Coherence is essential for longer texts, such as essays or articles, where maintaining a consistent narrative is key.

Factuality

Factuality evaluates the accuracy of the LLM's information, especially in information-seeking tasks. This metric confirms that the model generates text that is not only plausible but also factually correct.Factuality is indispensable for applications like news generation, educational content, and customer support, where providing accurate information is the main goal.

Evaluation Methodologies

A robust evaluation of LLMs involves integrating both quantitative and qualitative approaches. This section details a range of methodologies, such as:

• Benchmark datasets
• Human evaluation techniques
• Automated evaluation methods

To thoroughly assess LLM performance.

Benchmark Datasets

Benchmark datasets are valuable tools for evaluating LLMs. They provide standardized tasks that enable comparative analysis across different models. These datasets help establish a baseline for model performance and facilitate benchmarking.

Existing Benchmarks

Benchmark datasets are important tools for evaluating LLMs, providing standardized tasks that enable comparative analysis across different models. Some of the most popular benchmark datasets for various natural language processing (NLP) tasks include:

• Glue (General Language Understanding Evaluation): A collection of diverse tasks designed to evaluate the general linguistic capabilities of LLMs, including sentiment analysis, textual entailment, and question answering.
• Superglue: An advanced version of GLUE, comprising more challenging tasks to test the robustness and nuanced understanding of LLMs.
• Squad (Stanford Question Answering Dataset): A dataset focused on reading comprehension, where models are scored based on their ability to answer questions from Wikipedia articles.

Custom Datasets

While existing benchmarks are invaluable, creating custom datasets is vital for domain-specific evaluation. Custom datasets allow us to tailor the evaluation process to the unique requirements and challenges of the specific application or industry.For example, a healthcare organization could create a dataset of medical records and clinical notes to evaluate an LLM's ability to handle medical terminology and context. Custom datasets ensure the model's performance is aligned with real-world use cases, providing more relevant and actionable insights.

Human Evaluation

Human evaluation methods are indispensable for assessing the nuanced aspects of LLM outputs that automated metrics might miss. These techniques involve direct feedback from human judges, offering qualitative insights into model performance.

Direct Assessment

Human evaluation remains a gold standard for assessing the quality of LLM outputs. Direct assessment methods involve collecting feedback from human judges using surveys and rating scales.These methods can capture nuanced aspects of text quality, such as fluency, coherence, and relevance, which automated metrics might overlook. Human judges can provide qualitative feedback on specific strengths and weaknesses, helping to identify specific areas for improvement.

Comparative Judgment

Comparative judgment involves techniques like pairwise comparison, where human evaluators directly compare different models' outputs. This method can be more reliable than absolute rating scales, as it reduces the subjectivity associated with individual ratings.Evaluators are asked to choose the better output from pairs of generated texts, providing a relative ranking of model performance. Comparative judgment is particularly useful for fine-tuning models and selecting the best-performing variants.

Automated Evaluation

Automated evaluation methods provide a quick and objective way to assess LLM performance. These methods employ various metrics to quantify different aspects of model outputs, ensuring a comprehensive evaluation.

Metric-Based

Automated metrics provide a quick and objective way to evaluate LLM performance. Metrics like perplexity and BLEU are widely used to assess various aspects of text generation.As discussed earlier, perplexity measures the model's predictive capability, with lower scores indicating better performance. BLEU, on the other hand, evaluates the quality of generated text by comparing it to reference texts, focusing on the precision of n-grams.

Adversarial Evaluation

Adversarial evaluation involves subjecting LLMs to adversarial attacks to test their robustness. These attacks are designed to exploit weaknesses and biases in the model, revealing vulnerabilities that might not be apparent through standard evaluation methods. An adversarial attack might involve inputting slightly altered or misleading data to analyze how the model responds. This approach is useful for applications where reliability and security are held in high regard, as it helps to identify and mitigate potential risks.

Related Reading

• How to Fine Tune LLM
• How to Build Your Own LLM
• LLM Function Calling
• LLM Prompting
• What LLM Does Copilot Use
• LLM Use Cases
• LLM Sentiment Analysis
• LLM Evaluation Framework
• LLM Benchmarks
• Best LLM for Coding

Choosing Your Evaluation Metrics

The choice of which LLM evaluation metric to use depends on your LLM application's use case and architecture. For example, if you’re building a RAG-based customer support chatbot on top of OpenAI’s GPT models, you’ll need to use several RAG metrics:

• Faithfulness
• Answer Relevancy
• Contextual Precision

If you’re fine-tuning my Mistral 7B, I’d need metrics like bias to ensure impartial LLM decisions. 

RAG Metrics

Here is a great read for those who don’t know what RAG (Retrieval Augmented Generation) is. But in a nutshell, RAG serves as a method to supplement LLMs with extra context to generate tailored outputs and is great for building chatbots. It is made up of two components:

• The retriever
• The generator

Here’s How A Rag Workflow Typically Works:

• 1. Your RAG system receives an input.
• 2. The retriever uses this input to perform a vector search in your knowledge base (which nowadays in most cases is a vector database).
• 3. The generator receives the retrieval context and the user input as additional context to generate a tailored output.

Here’s One Thing To Remember: 

High-quality LLM outputs are the product of a great retriever and generator. For this reason, great RAG metrics focus on evaluating your RAG retriever or generator reliably and accurately. 

(RAG metrics were originally designed to be reference-less metrics, meaning they don’t require ground truths, making them usable even in a production setting.)

Faithfulness

Faithfulness is a RAG metric that evaluates whether the LLM/generator in your RAG pipeline is generating LLM outputs that factually align with the information presented in the retrieval context. But which scorer should we use for the faithfulness metric?

The QAG Scorer is the best scorer for RAG metrics since it excels for evaluation tasks where the objective is clear. For faithfulness, if you define it as the proportion of truthful claims made in an LLM output about the retrieval context, we can calculate faithfulness using QAG by following this algorithm:

• 1. Use LLMs to extract all claims made in the output.
• 2. For each claim, check whether it agrees or contradicts each node in the retrieval context. The close-ended question in QAG will be something like: 
  • Does the given claim agree with the reference text?
  • Where the “reference text” will be each individual retrieved node. 
  • (Note that you need to confine the answer to either:
    • Yes
    • No
    • Idk

The ‘idk’ state represents the edge case where the retrieval context does not

contain relevant information to give a yes/no answer.)

• 3. Add up the total number of truthful claims (‘yes’ and ‘idk’), and divide it by the total number of claims made.

This method ensures accuracy using LLM’s advanced reasoning capabilities while avoiding unreliability in LLM-generated scores, making it a better scoring method than G-Eval. DeepEval treats evaluation as test cases. Actual_output is simply your LLM output. Also, since faithfulness is an LLM-Eval, you can get a reason for the final calculated score.

Answer Relevancy

Answer relevancy is a RAG metric that assesses whether your RAG generator outputs concise answers. It can be calculated by determining the proportion of sentences in an LLM output that are relevant to the input (i.e., dividing the number of relevant sentences by the total number of sentences).The key to building a robust answer relevancy metric is considering the retrieval context since additional context may justify a seemingly irrelevant sentence’s relevancy. 

Contextual Precision

Contextual Precision is a RAG metric that assesses the quality of your RAG pipeline’s retriever. When talking about contextual metrics, we’re mainly concerned about the relevancy of the retrieval context. 

A high contextual precision score means relevant nodes in the retrieval context are ranked higher than irrelevant ones. This is important because LLMs give more weighting to information in nodes that appear earlier in the retrieval context, affecting the final output's quality.

Contextual Recall

Contextual Recall is another metric for evaluating a Retriever-Augmented Generator (RAG). It is calculated by determining the proportion of sentences in the expected output or ground truth that can be attributed to nodes in the retrieval context. 

A higher score represents a greater alignment between the retrieved information and the expected output, indicating that the retriever effectively sources relevant and accurate content to aid the generator in producing contextually appropriate responses.

Contextual Relevancy

Contextual relevancy is the simplest metric to understand: it is simply the proportion of sentences in the retrieval context that are relevant to a given input.

Fine-Tuning Metrics

Fine-tuning metrics mean metrics that assess the LLM itself rather than the entire system. Putting aside cost and performance benefits, LLMs are often fine-tuned to either: 

• 1. Incorporate additional contextual knowledge. 
• 2. Adjust its behavior.

Hallucination

Some of you might recognize this being the same as the faithfulness metric. Although similar, hallucination in fine-tuning is more complicated since pinpointing the exact ground truth for a given output is often difficult. 

To go around this problem, we can use SelfCheckGPT’s zero-shot approach to sample the proportion of hallucinated sentences in an LLM output. However, this approach can get very expensive, so for now, you would suggest using an NLI scorer and manually providing some context as the ground truth instead.

Toxicity

The toxicity metric evaluates the extent to which a text contains offensive, harmful, or inappropriate language. Off-the-shelf pre-trained models like Detoxify, which utilize the BERT scorer, can be employed to score toxicity.  

However, this method can be inaccurate since words “associated with swearing, insults or profanity are present in a comment, is likely to be classified as toxic, regardless of the tone or the intent of the author e.g. humorous/self-deprecating.” You might want to consider using G-Eval instead to define custom criteria for toxicity. The use case agnostic nature of G-Eval is the main reason why you will like it so much.

Bias

The bias metric evaluates political, gender, and social biases in textual content. This is particularly crucial for applications involving a custom LLM in decision-making processes, such as aiding in bank loan approvals with unbiased recommendations or in recruitment, where it assists in determining if a candidate should be shortlisted for an interview. Similar to toxicity, bias can be evaluated using G-Eval. 

Bias is highly subjective, varying significantly across geographical, geopolitical, and geosocial environments. For example, language or expressions considered neutral in one culture may carry different connotations in another. (This is also why few-shot evaluation doesn’t work well for bias.) A potential solution would be to fine-tune a custom LLM for evaluation or provide extremely clear rubrics for in-context learning. For this reason, bias is the hardest metric to implement.

Use Case Specific Metrics

In one of my previous articles, the summarization metric is in-depth, giving it a good read (and promise it's much shorter than this article). In summary (no pun intended), all good summaries: 

• Is factually aligned with the original text. 
• Includes important information from the original text.

Using QAG, we can calculate factual alignment and inclusion scores to compute a final summarization score.

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• LLM vs SLM
• SageMaker Alternatives
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