# Tracing Multilingual Knowledge Acquisition Dynamics in Domain Adaptation: A Case Study of English-Japanese Biomedical Adaptation

arXiv: https://arxiv.org/abs/2510.12115 · HTML: https://mlsanity.com/paper/2510.12115/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 43 references that are in the map.
- First posted on arXiv 14 Oct 2025.

## Builds on (most cited first, 27 of 27)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/1902.07669/) (2019)
- [Towards Continual Knowledge Learning of Language Models](https://mlsanity.com/paper/2110.03215/) (2021)
- [TemporalWiki: A Lifelong Benchmark for Training and Evaluating Ever-Evolving Language Models](https://mlsanity.com/paper/2204.14211/) (2022)
- [LLaMA Beyond English: An Empirical Study on Language Capability Transfer](https://mlsanity.com/paper/2401.01055/) (2024)
- [How Do Large Language Models Acquire Factual Knowledge During Pretraining?](https://mlsanity.com/paper/2406.11813/) (2024)
- [Multilingual Instruction Tuning With Just a Pinch of Multilinguality](https://mlsanity.com/paper/2401.01854/) (2024)
- [Instruction Pre-Training: Language Models are Supervised Multitask Learners](https://mlsanity.com/paper/2406.14491/) (2024)
- [Journey to the Center of the Knowledge Neurons: Discoveries of Language-Independent Knowledge Neurons and Degenerate Knowledge Neurons](https://mlsanity.com/paper/2308.13198/) (2023)
- [What does the Knowledge Neuron Thesis Have to do with Knowledge?](https://mlsanity.com/paper/2405.02421/) (2024)
- [PLUG: Leveraging Pivot Language in Cross-Lingual Instruction Tuning](https://mlsanity.com/paper/2311.08711/) (2023)
- [How do language models learn facts? Dynamics, curricula and hallucinations](https://mlsanity.com/paper/2503.21676/) (2025)
- [LLM-jp: A Cross-organizational Project for the Research and Development of Fully Open Japanese LLMs](https://mlsanity.com/paper/2407.03963/) (2024)
- [RomanSetu: Efficiently unlocking multilingual capabilities of Large Language Models via Romanization](https://mlsanity.com/paper/2401.14280/) (2024)
- [Multilingual Pretraining and Instruction Tuning Improve Cross-Lingual Knowledge Alignment, But Only Shallowly](https://mlsanity.com/paper/2404.04659/) (2024)
- [Romanization-based Large-scale Adaptation of Multilingual Language Models](https://mlsanity.com/paper/2304.08865/) (2023)
- [JParaCrawl v3.0: A Large-scale English-Japanese Parallel Corpus](https://mlsanity.com/paper/2202.12607/) (2022)
- [Tracing the Roots of Facts in Multilingual Language Models: Independent, Shared, and Transferred Knowledge](https://mlsanity.com/paper/2403.05189/) (2024)
- [Breaking the Script Barrier in Multilingual Pre-Trained Language Models with Transliteration-Based Post-Training Alignment](https://mlsanity.com/paper/2406.19759/) (2024)
- [Language-specific Neurons Do Not Facilitate Cross-Lingual Transfer](https://mlsanity.com/paper/2503.17456/) (2025)
- [Improving Domain Adaptation through Extended-Text Reading Comprehension](https://mlsanity.com/paper/2401.07284/) (2024)
- [ChatGPT as Data Augmentation for Compositional Generalization: A Case Study in Open Intent Detection](https://mlsanity.com/paper/2308.13517/) (2023)
- [What Matters in Memorizing and Recalling Facts? Multifaceted Benchmarks for Knowledge Probing in Language Models](https://mlsanity.com/paper/2406.12277/) (2024)
- [Pre-training data selection for biomedical domain adaptation using journal impact metrics](https://mlsanity.com/paper/2409.02725/) (2024)
- [Neuron Empirical Gradient: Discovering and Quantifying Neurons Global Linear Controllability](https://mlsanity.com/paper/2412.18053/) (2024)
- [LEIA: Facilitating Cross-lingual Knowledge Transfer in Language Models with Entity-based Data Augmentation](https://mlsanity.com/paper/2402.11485/) (2024)

## Abstract (arXiv)

Multilingual domain adaptation (ML-DA) is widely used to learn new domain knowledge across languages into large language models (LLMs). Although many methods have been proposed to improve domain adaptation, the mechanisms of multilingual knowledge acquisition, how domain knowledge is learned within a language and transferred across languages, remain underexplored. This gap leads to suboptimal performance, particularly in low-resource settings. This work examines the learning dynamics of LLMs during ML-DA. Because prior ML-DA studies often train and evaluate on datasets with mismatched knowledge coverage, we propose AdaXEval, an adaptive evaluation method that builds multiple-choice QA datasets from the same bilingual domain corpus used for training, thereby directly studying multilingual knowledge acquisition. Through continual training of LLMs with diverse data recipes, we track how LLMs acquire domain facts and pinpoint the mechanism behind the transformation process from domain training data to knowledge. Our experiments on a 13B English-Japanese bilingual LLM reveal that cross-lingual transfer remains challenging despite a high-quality bilingual corpus. The code has been released.