Master Thesis: Evaluating synthetic data generation for delta learning

As Sweden's national center for applied AI, we're on a mission to accelerate the use of AI to benefit our society, our competitiveness, and everyone living in Sweden. We drive impactful initiatives in areas such as healthcare, energy, and public services while pushing the boundaries of AI research in fields such as natural language processing, machine learning and AI security. Join us in harnessing the untapped value of AI to drive innovation and create sustainable value for Sweden.

We are now looking for a master thesis student to further strengthen our NLU team.

Project Background and Problem Statement

AI Sweden and OpenEuroLLM are currently training Large Language Models for all European Languages. Machine translated or synthetic data will be necessary to cover all languages, at all stages of training, but machine translated data will introduce some amount of translationese or poor fluency. Delta learning offers a potential way to mitigate this: By isolating the unwanted quality (lack of fluency / translationese) and generating or identifying documents that differ in that quality alone. The core question is:

• Can Delta Learning be employed in this way? How to source data such that it differs only in the desired quality? And what (if any) are the adverse effects on other tasks, if this is applied? (As measured on MMLU ProX or Belebele).

Who we’re looking for

We are seeking curious, self-driven MSc students eager to work at the frontier of open-weight European AI research (LLMs). You thrive on empirical discovery, design rigorous experiments, and let data challenge your assumptions.

• Ongoing Master’s studies in Computer Science, Data Science, Machine Learning, Engineering Physics, or a related quantitative field.

• Proficiency in Python and hands-on experience with modern deep learning frameworks (PyTorch, Hugging Face ecosystem).

• Familiarity with LLM post-training alignment (e.g., SFT, DPO, RLHF/RLVR) or context-extension, alongside comfort running distributed GPU training in Linux/HPC environments.

At AI Sweden, we are committed to building diverse and inclusive teams. Some positions may be subject to export control regulations, which means that specific requirements may apply.

If you have any questions or thoughts, don’t hesitate to contact

Amaru Cuba Gyllensten, Senior Research Scientist

Anna Lokrantz, AI Engineer

AI Sweden does not accept unsolicited support and kindly ask not to be contacted by any advertisement agents, recruitment agencies or manning companies.

References

[1] Rafailov, R., et al., "Direct Preference Optimization: Your Language Model is Secretly a Reward Model," NeurIPS 2023.

[2] Tunstall, L., et al., "Zephyr: Direct Distillation of LM Alignment," arXiv 2023.