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. Introduction As large language models (LLMs) scale, gathering high-quality human preference data for instruction tuning becomes a critical bottleneck. In OpenEuroLLM, we are using natural, synthetic, and translated data. Recent experiments indicate that models trained on translated data often suffer from "translationese" artifacts, masking native fluency. Delta learning has recently been put forward as an approach for steering models towards specific qualities: Using DPO to train models on datapoints that contrast in a single qualitative dimension we can steer model behaviour in that dimension. One such dimension is fluency: Can we synthesize documents that differ only in the fluency/translationese dimension, so that we can steer models trained on less fluent data towards being more fluent, while retaining other capabilities? 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).

Outline The goal of this project is to rigorously evaluate DPO data synthesis strategies on Prelude 9B.

• Literature study: Review DPO methodologies, synthetic data generation pipelines, and LLM evaluation benchmarks focusing on translationese artifacts.
• Implementation: Construct controlled DPO training runs using Prelude 9B. Compare a baseline before fluency mitigation against runs after fluency mitigation, comparing the effect of data choices, training hyperparameters, et.c. on chosen benchmarks.
• Evaluation: Evaluate fluency, safety, and adequacy using JudgeArena and standard multilingual benchmarks to measure the Elo rating improvements.

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. Why should you do your thesis with AI Sweden? Doing your thesis at AI Sweden means working alongside leading AI scientists and change leaders. AI Sweden is Sweden’s National Center for AI, we drive research questions that have both a long shelf-life and are widely applicable to Swedish industry and the public sector. We aim for publications at the most competitive venues and celebrate a culture of research excellence. As an organization, we’re uniquely positioned at the sweet spot of governmental influence and startup agility. Small enough to stay adaptive and have fun but backed by and in close contact with both the government, academia and private and public sector. Practical details Location: Hybrid (Gothenburg / Stockholm) or Remote. Application Deadline: 2026-11-20 (rolling selection – position may be filled earlier). Start Date: January 2027 Contact 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.