Welcome to the final lesson of the "Scoring LLM Outputs with Logprobs and Perplexity" course. In previous lessons, you explored how to extract log probabilities and calculate perplexity to evaluate language models. Building on that foundation, this lesson will focus on comparing the fluency of different language models. Model fluency is a crucial aspect of evaluating how well a model can generate coherent and natural-sounding text. By the end of this lesson, you will be able to assess model fluency using log probabilities and perplexity, providing you with a deeper understanding of model performance.

Before we dive into the code, let's ensure your environment is ready. If you're working on your local machine, you'll need to install the openai library. You can do this using pip:

The math library is part of Python's standard library, so no installation is needed for it. However, if you're using the CodeSignal environment, these libraries are already pre-installed, allowing you to focus on the code without worrying about setup.

Let's break down the code snippet you'll be working with. This code is designed to evaluate the fluency of a sentence across different language models using log probabilities obtained from OpenAI's API. We start by importing the necessary libraries: math for mathematical operations and OpenAI for interacting with the language model. Next, we initialize the OpenAI client, which allows us to send requests to the model. We define a list of models and a sentence that we want to evaluate. The code processes the sentence for each model individually, creating a chat completion request for each one. This request specifies the model to use, the message content, and parameters such as max_tokens, logprobs, and top_logprobs. These parameters control the number of tokens generated, whether to return log probabilities, and how many top token probabilities to retrieve, respectively.

Now, let's see the code in action with a practical example. We have a sentence: "The president addressed the nation on live television." By running the code, we can evaluate the fluency of this sentence across different models based on the log probabilities of the first token generated by each model.

When you run this code, you might see an output similar to:

In this example, the gpt-4 model has a lower perplexity, indicating that it finds the sentence more fluent and predictable compared to gpt-3.5-turbo. This demonstrates how you can use log probabilities and perplexity to compare the fluency of different models.

In this lesson, you learned how to evaluate and compare the fluency of different language models using log probabilities and perplexity. You explored the code structure and saw a practical example of assessing sentence fluency across models. By understanding and interpreting these metrics, you can gain deeper insights into model performance and fluency. As you move on to the practice exercises, I encourage you to experiment with different sentences and models to deepen your understanding. Congratulations on reaching the end of the course! Your dedication and effort have equipped you with advanced techniques for evaluating language models. Keep practicing and exploring to master these skills. Good luck!