Generating the Evaluation Code in Python

Behind the scenes

Text-to-code generation might seem like a trivial problem to solve, given how well LLMs can generate text from textual prompts. However, LLMs’ text-to-code generation relies on extensive training and fine-tuning of models to understand and generate both natural language and code. Here are some key differences between a text-to-text model and a text-to-code model:

• Complexity: Code generation requires understanding programming syntax, logic, and how different code parts interact. This makes it more complex than text-to-text models, which primarily deal with the semantics and structure of human languages.

• Output Format: Text-to-code models generate code snippets or a complete program in a specific programming language, as compared to plain text for text-to-text models.

• Application: Text-to-code models can automate repetitive coding tasks, assist programmers, or help beginners learn. Text-to-text models, on the other hand, are used for tasks such as creative content writing, information summarization, or language translation.

Gemini currently supports around 20 programming languages. ...