A major challenge in automated test generation is the readability of generated tests. Emerging large language models (LLMs) excel at language analysis and transformation tasks. We propose that improving test readability is such a task and explore the capabilities of the GPT-4 LLM in improving readability of tests generated by the Pynguin search-based generation framework. Our initial results are promising. However, there are remaining research and technical challenges.