As Jensen Huang said during his keynote at Data+AI summit, "Generative AI is just everywhere, every single industry. If your industry's not involved in generative AI, it's just because you haven't been paying attention."
But being widespread doesn't mean these models are flawless. In real business use cases, models very often miss the mark and need refinement. That's where LLM evaluations come in: to ensure models are reliable, accurate, and meet business preferences.
In this article, we'll dive into why evaluating LLMs is crucial and explore LLM evaluation metrics, frameworks, tools, and challenges. We'll also share some solid strategies we've crafted from working with our customers and share the best practices.
What is LLM evaluation?
LLM evaluation is the process of testing and measuring 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 and clearly they generate text, and whether their responses make sense in context. This step is super important because it helps us catch any issues and improve the model, ensuring it can handle tasks effectively and reliably before it goes live.
Why do you need to evaluate an LLM?
It's simple: to make sure the model is up to the task and its requirements. Evaluating an LLM ensures it understands and responds accurately, handles different types of information correctly, and communicates in a way that's safe, clear, and effective. 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. Then, 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?
As you collect data from these simulations, you're also building a valuable dataset. 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 making improvements helps the model work better. It makes sure 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 what customers actually need. You start by figuring out the industry's unique challenges and goals. Then, create test scenarios that mirror the real tasks the model will face, whether answering customer service questions, analyzing data, or writing content that strikes the right chord.
You also need to 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 really 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, and MMLU, using well-known metrics.
However, these LLMs might not perfectly fit your specific needs straight out of the box. Sometimes, we need to fine-tune the LLM with a unique dataset crafted just 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 remember, ensuring that an LLM works as expected isn't just about the model itself; it's also about 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, it's crucial to ensure the LLM delivers the desired results.
LLM evaluation metrics
There are several LLM evaluation metrics that practitioners use to measure how well the model performs.
Perplexity
Perplexity measures how well a model predicts a sample of text. A lower score means better performance. It calculates the exponential of the average log-likelihood of a sample:
Perplexity=exp(−1N∑logP(xi))Perplexity=exp(−N1∑logP(xi))
where NN is the number of words and P(xi)P(xi) is the probability the model assigns to the i-th word.
While useful, perplexity doesn't tell us about the text's quality or coherence and can be affected by how the text is broken into tokens.
BLEU Score
Originally for machine translation, the BLEU score is now also used to evaluate text generation. It compares the model's output to reference texts by looking at the overlap of n-grams.
Scores range from 0 to 1, with higher scores indicating a better match. However, BLEU can miss the mark in evaluating creative or varied text outputs.
ROUGE
ROUGE is great for assessing summaries. It measures how much the content generated by the model overlaps with reference summaries using n-grams, sequences, and word pairs.
F1 Score
The F1 score is used for classification and question-answering tasks. It balances precision (relevance of model responses) and recall (completeness of relevant responses):
F1=2×(precision×recall)precision+recallF1=precision+recall2×(precision×recall)
It ranges from 0 to 1, where 1 indicates perfect accuracy.
METEOR
METEOR considers not just exact matches but also synonyms and paraphrases, aiming to align better with human judgment.
BERTScore
BERTScore evaluates texts by comparing the similarity of contextual embeddings from models like BERT, focusing more on meaning than exact word matches.
Levenshtein distance, or edit distance, measures the minimum number of single-character edits (insertions, deletions, or substitutions) needed to change one string into another. It's valuable for:
- Assessing text similarity in generation tasks
- Evaluating spelling correction and OCR post-processing
- Complementing other metrics in machine translation evaluation
A normalized version (0 to 1) allows for comparing texts of different lengths. While simple and intuitive, it doesn't account for semantic similarity, making it most effective when used alongside other evaluation metrics.
Human evaluation
Despite the rise of automated metrics, human evaluation is still vital. Techniques include using Likert scales to rate fluency and relevance, A/B testing different model outputs, and expert reviews for specialized areas.
Task-specific metrics
For tasks like dialogue systems, metrics might include engagement levels and task completion rates. For code generation, you'd look at how often the code compiles or passes tests.
Robustness and fairness
It's important to test how models react to unexpected inputs and to assess for bias or harmful outputs.
Efficiency metrics
As models grow, so does the importance of measuring their efficiency in terms of speed, memory use, and energy consumption.
AI evaluating AI
As AI gets more advanced, we're beginning to use one AI to evaluate another. This method is fast and can handle massive amounts of data without tiring. Plus, AI can spot complex patterns that humans might overlook, offering a detailed look at performance.
However, it's not perfect. AI evaluators can be biased, sometimes favoring certain responses or missing subtle context that a human would catch. There's also a risk of an "echo chamber," where AI evaluators favor responses similar to what they're programmed to recognize, potentially overlooking unique or creative answers.
Another issue is that AI often can't explain its evaluations well. It might score responses but not offer the in-depth feedback a human would, which can be like getting a grade without knowing why.
Many researchers find that mixing AI with human evaluation works best. AI handles the bulk of data processing, while humans add essential context and insight.
Top 10 LLM evaluation frameworks and tools
There are practical frameworks and tools on the internet that you can use to build your eval dataset.
SuperAnnotate
SuperAnnotate helps companies build their eval and fine-tuning datasets to improve model performance. Its fully customizable editor enables building datasets for any use case in any industry.
Amazon Bedrock
Amazon's entry into the LLM space – Amazon Bedrock – includes evaluation capabilities. It's particularly useful if you're deploying models on AWS. SuperAnnotate integrates with Bedrock, allowing you to build data pipelines using SuperAnnotate's editor and fine-tune models with Bedrock.
Nvidia Nemo
Nvidia Nemo is a cloud-based microservice designed to automatically benchmark both state-of-the art foundation and also custom models. It evaluates them using a variety of benchmarks, which include those from academic sources, customer submissions, or using LLMs as judges.
Azure AI Studio
Microsoft's Azure AI Studio provides a comprehensive suite of tools for evaluating LLMs, including built-in metrics and customizable evaluation flows. It's particularly useful if you're already working within the Azure ecosystem.
Prompt Flow
Another Microsoft tool, Prompt Flow allows you to create and evaluate complex LLM workflows. It's great for testing multi-step processes and iterating on prompts.
Weights & biases
Known for its experiment tracking capabilities, W&B has expanded into LLM evaluation. It's a solid choice if you want to keep your model training and evaluation in one place.
LangSmith
Developed by Anthropic, LangSmith offers a range of evaluation tools specifically designed for language models. It's particularly strong in areas like bias detection and safety testing.
TruLens
TruLens is an open-source framework that focuses on transparency and interpretability in LLM evaluation. It's a good pick if you need to explain your model's decision-making process.
Vertex AI Studio
Google's Vertex AI Studio includes evaluation tools for LLMs. It's well-integrated with other Google Cloud services, making it a natural choice for teams already using GCP.
DeepEval
Deep Eval is an open-source library that offers a wide range of evaluation metrics and is designed to be easily integratedinto existing ML pipelines.
Parea AI
Parea AI focuses on providing detailed analytics and insights into LLM performance. It's particularly strong in areas like conversation analysis and user feedback integration.
LLM model evaluation benchmarks
To check how language models handle different tasks, researchers and developers use a set of standard tests. Here are some of the main benchmarks they use:
GLUE (General Language Understanding Evaluation)
GLUE tests an LLM's understanding of language with nine different tasks, such as analyzing sentiments, answering questions, and figuring out if one sentence logically follows another. It gives a single score that summarizes the model's performance across all these tasks, making it easier to see how different models compare.
SuperGLUE
As models began to beat human scores on GLUE, SuperGLUE was introduced. It's a tougher set of tasks that pushes models to handle more complex language and reasoning.
HellaSwag
HellaSwag checks if an LLM can use common sense to predict what happens next in a given scenario. It challenges the model to pick the most likely continuation out of several options.
TruthfulQA
TruthfulQA is all about honesty. It tests whether a model can avoid giving false or misleading answers, which is essential for creating reliable AI.
MMLU (Massive Multitask Language Understanding)
MMLU is vast, covering everything from science and math to the arts. It has over 15,000 questions across 57 different tasks. It's designed to assess how well a model can handle a wide range of topics and complex reasoning.
Other Benchmarks
There are more tests, too, like:
- ARC (AI2 Reasoning Challenge): Focuses on scientific reasoning.
- BIG-bench: A collaborative project with many different tasks.
- LAMBADA: Tests how well models can guess the last word in a paragraph.
- SQuAD (Stanford Question Answering Dataset): Measures reading comprehension and ability to answer questions.
LLM evaluation best practices
SuperAnnotate's VP of LLMs Ops, Julia MacDonald, shares her insights on the practical side of LLM evaluations: "Building an evaluation framework that's thorough and generalizable, yet straightforward and free of contradictions, is key to any evaluation project's success."
Her perspective underlines the importance of establishing a strong foundation for evaluation. Based on our experience with customer datasets, we've developed several practical strategies:
Choosing the right human evaluators: It's important to pick evaluators who have a deep understanding of the areas your LLM is tackling. This ensures they can spot nuances and judge the model's output effectively.
Setting clear evaluation metrics: Having straightforward and consistent metrics is key. Think about what really matters for your model—like how helpful or relevant its responses are. These metrics need to be agreed upon by parties involved, making sure they match the real-world needs the LLM serves.
Running continuous evaluation cycles: Regular check-ins on your model's performance help catch any issues early on. This ongoing process keeps your LLM sharp and ready to adapt.
Benchmarking against the best: It's helpful to see how your model performs against industry standards. This highlights where you're leading the pack and where you need to double down your efforts.
Choosing the right people to help build your eval dataset is key, and we'll dive into that in the next section.
LLM Evaluations in SuperAnnotate
SuperAnnotate helps companies build and refine their evaluation datasets to match their specific needs. With our user-friendly editor, powerful tools, and expert data trainers, we ensure every customer gets the ideal training dataset they're looking for.
We bring the best practices into our work, making SuperAnnotate the trusted partner for businesses looking to fine-tune their models. Our customers choose us to develop their eval datasets for these reasons:
Selecting top-tier expert evaluators: We work with some of the industry's most skilled data trainers. Our evaluators are experienced and detail-oriented.
Jointly defining stable evaluation metrics: We use clear and consistent evaluation criteria, such as helpfulness, harmlessness, relevancy, etc., to measure the model's performance.
Creating reserved prompt sets (Optional): We have special prompt sets reserved for testing, which have never been part of our training process. These sets can mirror desired complexity levels based on existing public eval sets (e.g., MT Bench).
Implementing continuous evaluation cycles: Our evaluation is an ongoing process. We do regular blind evaluation cycles to track model performance trends over time. This feedback loop makes sure the models continue to meet and exceed what our users need.
Benchmarking against industry standards: We regularly compare the model's performance against industry standards to ensure they not only meet but lead in performance, keeping us ahead in the market.
Bias prevention: SuperAnnotate's platform can be easily customized to remove bias risks. This can include setups like obfuscating model versions, randomizing the location of model outputs, ELO scores from an "arena" setting, and other techniques.
LLM evaluation challenges
Evaluating big language models can be tricky for a few reasons.
Training data overlap
It's tough to make sure the model hasn't seen the test data before. With LLMs trained on massive datasets, there's always a risk that some test questions might have been part of their training (overfitting). This can make the model seem better than it really is.
Metrics are too generic
We often lack good ways to measure LLMs' performance across different demographics, cultures, and languages. They also mainly focus on accuracy and relevance and ignore other important factors like novelty or diversity. This makes it hard to ensure the models are fair and inclusive in their capabilities.
Adversarial attacks
LLMs can be fooled by carefully crafted inputs designed to make them fail or behave unexpectedly. Identifying and protecting against these adversarial attacks with methods like red teaming is a growing concern in evaluation.
Benchmarks aren't for real-world cases
For many tasks, we don't have enough high-quality, human-created reference data to compare LLM outputs against. This limits our ability to accurately assess performance in certain areas.
Inconsistent performance
LLMs can be hit or miss. One minute, they're writing like a pro; the next they're making silly mistakes and hallucinating. This up-and-down performance makes it hard to judge how good they really are overall.
Too good to measure
Sometimes LLMs produce text that's as good as or better than what humans write. When this happens, our usual ways of scoring them fall short. How do we rate something that's already top-notch?
Missing the mark
Even when an LLM gives factually correct information, it might completely miss the context or tone needed. Imagine asking for advice and getting a response that's technically right but totally unhelpful for your situation.
Narrow testing focus
Many researchers get caught up in tweaking the model itself and forget about improving how we test it. This can lead to using overly simple metrics that don't tell the whole story of what the LLM can really do.
Human judgment challenges
Getting humans to evaluate LLMs is valuable but comes with its own problems. It's subjective, can be biased, and is expensive to do on a large scale. Plus, different people might have very different opinions about the same output.
AI grader's blind spots
When we use other AI models to evaluate LLMs, we run into some odd biases. These biases can skew the results in predictable ways, making our evaluations less reliable. Automated evaluations aren't as objective as we think. We need to be aware of blind spots to get a fair picture of how an LLM is really performing.
Closing Remarks
In a nutshell, evaluating large language models is essential if we want to understand and enhance their capabilities fully. This understanding doesn't just help us fix current issues but also guides the development of more reliable and effectiveAI applications. As we move forward, the focus on improving evaluation techniques will play a crucial role in ensuring that AI tools can perform accurately and ethically in various settings. This ongoing effort will help pave the way for AI that genuinely benefits society, making each evaluation step a significant stride toward a future where AI and humans collaborate seamlessly.
