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. However, given its large context window and its ability to reason about logic, Gemini can be used to generate and query code that might not be officially supported.

Generating code with LLMs