Welcome to this lesson on comparing different large language models (LLMs) using smart scoring. In the previous lesson, we explored the concept of fuzzy matching to improve the evaluation of model responses. Today, we will build on that knowledge to compare multiple models: GPT-3.5-turbo, GPT-4, and GPT-4-turbo. The goal is to generate a leaderboard that ranks these models based on their performance in answering questions from the TriviaQA dataset. This comparison will help you understand the strengths and weaknesses of each model, enabling you to make informed decisions about which model to use for specific tasks. We will employ few-shot learning for all models to enhance their performance by providing a few examples in the prompts.

As a reminder, fuzzy scoring is a technique used to measure the similarity between two pieces of text. This approach is particularly useful in question-answering evaluations, where minor variations in wording can lead to incorrect assessments. In Python, we use the SequenceMatcher from the difflib library to calculate a similarity ratio between two strings. A ratio closer to 1 indicates high similarity, while a ratio closer to 0 indicates low similarity. By setting a threshold, we can determine what level of similarity is acceptable for considering two responses as equivalent. This method allows for more flexible and reliable evaluation of model responses.

To evaluate the models, we will use a Python script that processes the TriviaQA dataset and queries each model with trivia questions. The script is structured to read the dataset, query the models, and evaluate their responses using fuzzy scoring. While the CodeSignal environment has the necessary libraries pre-installed, you should be aware of how to set up your environment on personal devices. This involves installing the openai library and ensuring you have access to the TriviaQA dataset.

Let's walk through the code example provided in the OUTCOME section. The script begins by defining a function is_similar that uses SequenceMatcher to determine the similarity between two strings. This function takes two strings, a and b, and a similarity threshold. If the similarity ratio exceeds the threshold, the function returns True, indicating that the strings are similar enough to be considered equivalent.

Next, the script defines a function query_model that queries different models with prompts. For the "gpt-4-turbo" model, it uses openai.ChatCompletion.create, similar to other models. This function returns the model's response to the given prompt. We will incorporate few-shot learning by including a few examples in the prompts to improve the models' understanding and response accuracy.

The script then reads the TriviaQA dataset and initializes a dictionary to store the results for each model. It iterates over the models and the question-answer pairs, querying each model with a prompt and evaluating the response using the is_similar function. If the response is similar to the expected answer, the model's score is incremented.

After evaluating the models, the script generates a leaderboard by sorting the results based on the scores. The leaderboard ranks the models from highest to lowest score, providing a clear comparison of their performance. Here's an example of what the output might look like:

This output indicates that GPT-4 performed the best, followed by GPT-4-turbo and GPT-3.5-turbo. By interpreting these results, you can gain insights into each model's capabilities and choose the most suitable model for your specific needs.

In this lesson, we built on the concept of fuzzy scoring to evaluate and compare multiple LLMs. You learned how to implement a script that queries different models, evaluates their responses, and generates a leaderboard to rank their performance. This process provides a comprehensive understanding of each model's strengths and weaknesses, enabling you to make informed decisions about model selection. We also introduced few-shot learning to enhance model performance by providing examples in the prompts.

As you move forward, practice these concepts with the exercises provided. Experiment with different similarity thresholds and observe how they affect the evaluation accuracy. This hands-on experience will reinforce your understanding and prepare you for more advanced evaluation techniques in future lessons. Keep up the great work, and continue to apply your newfound skills in real-world scenarios.