Beyond fine-tuning of OpenAI models Importantly, we are also not limited to fine-tuning; in Supplementary Note 6, we show that we can even achieve good performance without finetuning by incorporating examples directly into the prompt (so-called in-context learning, i.e., learning during inference time). This works particularly well with the largest GPT-3 models and GPT-4. We are also not limited to using models from OpenAI. In ?? and Supplementary Note 8, we also show that we could obtain good results by fine-tuning the open-source LLMs parameter-efficient fine-tuning techniques on consumer hardware and provide a Python package that makes it easy to apply this approach to new problems.