Understanding LLM Evaluation: Why It Matters
LLM evaluation is the process of testing and measuring how well large language models perform in real-world situations.
What Are We Looking For?
When evaluating LLMs, we focus on three key areas:
- Comprehension: How well does the model actually understand what we're asking? Can it grasp nuances and context, or does it just pattern-match keywords?
- Output Quality: Is the text it generates clear, coherent, and natural-sounding?
- Contextual Accuracy: Does the model stay on topic and provide relevant responses? A technically perfect answer that misses the point isn't very helpful.
Why It Matters
- Identify and fix potential problems before they affect users
- Ensure the model performs consistently across different scenarios
- Fine-tune the model's responses for better real-world applications
Example Evaluation
Start by asking the LLM to respond to common queries, and use real world previous examples. Check if the LLMs responses are accurate, clear, helpful. Ensure the model understands the question, the context, and is the information is provides correct. If the prompt if too complex or not clear, does the model ask clarifying questions.
This process will help you generate an evaluation dataset which can be used for fine-tuning and RLHF.
Custom LLM evaluations allow developers to focus on metrics truly relevant to the problem.
LLM model vs system evaluation
Models are often tested against standard benchmarks like GLUE, SuperGLUE, HellaSwag, TruthfulQA, and MMLU, using well-known metrics. Fine-tuned models generally need there own evaluation data and methods in order to measure the success of the fine-tuning.
Evaluation is not just about the model itself. We also need to evaluate the prompt templates, data retrieval systems, and the model architecture if necessary.
LLM Evaluation Metrics
Perplexity
Perplexity measures how well a model predicts a sample of text. A lower score means better performance. 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).
Human evaluation
Techniques include using Likert scales to rate fluency and relevance, A/B testing different model outputs, and expert reviews for specialised areas.
Task Specific
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
LLMs are now being used to evaluate each other (e.g ). This can be fast and handle large amounts of data. Plus LLMs can often evaluate more complex patterns that the above approaches miss. But AI methods can show a tendency towards bias, or missing problems that humans may spot. Also LLMs may tend to prefer responses that sound like they are generated by LLMs. LLMs often struggle to explain their evaluations, failing to offer any detailed feedback. Generally AI + human works best for evaluations.
LLM model evaluation benchmarks
GLUE (General Language Understanding Evaluation) and SuperGLUE
GLUE tests an LLM's understanding of language with nine different tasks, such as analysing sentiments, answering questions, and next sentence entailment. It gives a single score that summarises the model's performance across all these tasks, making it easier to see how different models compare.
SuperGLUE is a tougher set of tasks that pushes models to handle more complex language and reasoning.
Best Practices
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. 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.
LLM evaluation challenges
Training data overlap
LLMs are trained on massive datasets, meaning there's always a risk that some test questions might have been part of their training leading to overfitting.
Metrics are too generic
Often the metrics focus on improving the average response rather than response for a certain group. They also mainly focus on accuracy and relevance and ignore other important factors like novelty or diversity.
Adversarial attacks
LLMs can be fooled by carefully crafted inputs designed to make them fail or behave unexpectedly.
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.
Inconsistent performance
LLM performance can be highly variable, often hallucinating facts.
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.
Missing the mark
Even when an LLM gives factually correct information, it might completely miss the context or tone needed.
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.
