Companies today speed up the machine learning process by fine-tuning large language models. You can go a long way to adapting these models to your needs. However, you need to be sure that your fine-tuning is successful. Considering how LLMs are evolving, this means carefully monitoring your results and tweaking them as necessary.
In this post, we’ll look at how you can evaluate the results with specific metrics and benchmarks.
What is LLM?
An LLM, or large language model uses the power of AI to create new text. The advantage of these models is that they can write much like humans can. This makes them useful for applications like customer support chatbots.
Key Evaluation Metrics for Fine-Tuned LLMs
The metrics you use to assess the model will depend on the final task. However, you may need to combine more than one. Let’s look at some of the most relevant options.
Perplexity (PPL)
This is one of the most common ways to measure performance. It looks at how well your model can predict the sequence of words. More specifically, it looks at how the model categorizes the test data. The lower the perplexity score, the more accurate the model’s predictive ability is.
This metric is especially useful when evaluating tasks like next-word prediction and language modeling. In these cases, the goal is to evaluate how coherent and contextually relevant the text is.
The shortcoming of perplexity is that it can’t measure deeper semantic understanding. It might also be problematic with tasks like summarizing and translation.
Accuracy
You’ll use this metric when it comes to classification tasks. This can be for entity recognition, text classification, or sentiment analysis. This measurement evaluates how many correct labels the model predicts. It’s also an easy way to assess multi-class or binary classification tasks.
However, you’ll need something else when it comes to imbalanced datasets. You can then use precision, recall, and the F1-score. It’s a good idea to use different metrics where possible.
Precision, Recall, and F1-Score
Most people use these in combination with accuracy. Doing so gives you a more nuanced understanding of how your LLM fine-tuning is going:
- Precision: This looks at the proportion of true positive predictions out of all of them. It’s a useful metric if you can’t afford to make mistakes.
- Recall: This is another way of measuring true positives. It’s another measurement useful when you need high accuracy, like medical diagnostic tasks.
- F1-Score: This gives you the average between precision and recall. It’s a good overall measurement to use when you’re working with imbalanced datasets.
BLEU (Bilingual Evaluation Understudy)
This is one of the most widely used metrics for checking text generation tasks. It’s particularly useful in text summarizing and translation. It checks the overlap between the text your model generates and a reference text.
It works in a measurement called n-grams. This is the continuous sequence of n words. BLEU is useful to check how similar the output is to human-generated text.
However, it’s limited. It can also unfairly penalize alternate phrasing because it doesn’t understand the text properly. Still, it’s one of the most common metrics you’ll come across.
ROUGE (Recall-Oriented Understudy for Gisting Evaluation)
This is another metric that people use with machine translation and text summarization tasks. Like, BLEU, it measures n-grams. However, it also looks at the context and makes sure that it covers all the key information.
Exact Match (EM)
We usually use this measurement in tasks like question-answering. It checks that the output matches the reference answer exactly. It’s useful when you’re training a service bot.
It’s a very strict measure, so you may find it lacking where the answers are phrased differently. To get a more complete picture, you can use other measurements with it.
Mean Reciprocal Rank (MRR)
Are you working on a project based on information retrieval, like a recommendation system? Then, you need to use an MMR. This measurement ranks the correct answer in the output. The higher the rank of the correct answer, the better your model is doing.
It’s a great option when your model needs to list responses and rank them.
Benchmarks for Evaluating Fine-Tuned LLMs
Now we’ve looked at the metrics, let’s move on. In this section, we’ll look at the benchmarks you might compare your model against. These are recognized datasets that anyone can use.
GLUE (General Language Understanding Evaluation)
This popular benchmark covers a wide range of NLP tasks like:
- Sentiment analysis
- Question-answering
- Natural language inference
You’ll feed in the data and see how your model performs. It’s a valuable test of their generalization capabilities. The higher the score, the better.
SuperGLUE
This is an extension of GLUE. It’s meant to present more of a challenge. It includes additional tasks that test your language model’s capacity. This can include common-sense reasoning and sentence completion.
This helps you push the boundaries of your model. The state-of-the-art LLMs use this benchmark.
SQuAD (Stanford Question Answering Dataset)
This benchmark evaluates models that answer questions. It provides a text passage and gets your model to generate answers. The more accurate the output, the better your LLM will score.
MS MARCO
This is a large-scale benchmark that tests your model’s ability to retrieve information. It’s helpful if you’re testing a search engine-like app. The dataset poses real-world questions. It then marks the accuracy of the answers.
MT-Bench
This benchmark measures multi-task learning. It incorporates tests to see how well your model performs while completing different jobs at once.
If you need help with this kind of data collection, or anything related to it, you should seek advice from companies with a proven track record.
Conclusion
There are a number of benchmarks and metrics against which to measure your fine-tuned LLM. We’ve gone over the main ones here so that you can properly assess your model’s strengths and weaknesses.
It’s important to choose the right ones to get the best results. This may mean using a combination of several measurements.