# Introducing Code-Switched Contexts to Cognitively-Inspired Bilingual Model Training

arXiv: https://arxiv.org/abs/2610.06161 · HTML: https://mlsanity.com/paper/2610.06161/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 62 references that are in the map.
- First posted on arXiv 5 Oct 2026.

## Builds on (most cited first, 18 of 18)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Similarity of Neural Network Representations Revisited](https://mlsanity.com/paper/1905.00414/) (2019)
- [XNLI: Evaluating Cross-lingual Sentence Representations](https://mlsanity.com/paper/1809.05053/) (2018)
- [Word Translation Without Parallel Data](https://mlsanity.com/paper/1710.04087/) (2017)
- [The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants](https://mlsanity.com/paper/2308.16884/) (2023)
- [SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects](https://mlsanity.com/paper/2309.07445/) (2023)
- [MultiBLiMP 1.0: A Massively Multilingual Benchmark of Linguistic Minimal Pairs](https://mlsanity.com/paper/2504.02768/) (2025)
- [Code-Switching Curriculum Learning for Multilingual Transfer in LLMs](https://mlsanity.com/paper/2411.02460/) (2024)
- [A Bit of a Problem: Measurement Disparities in Dataset Sizes Across Languages](https://mlsanity.com/paper/2403.00686/) (2024)
- [Investigating and Scaling up Code-Switching for Multilingual Language Model Pre-Training](https://mlsanity.com/paper/2504.01801/) (2025)
- [MuBench: Assessment of Multilingual Capabilities of Large Language Models Across 61 Languages](https://mlsanity.com/paper/2506.19468/) (2025)
- [BabyLM Turns 4 and Goes Multilingual: Call for Papers for the 2026 BabyLM Workshop](https://mlsanity.com/paper/2602.20092/) (2026)
- [XCOMPS: A Multilingual Benchmark of Conceptual Minimal Pairs](https://mlsanity.com/paper/2502.19737/) (2025)
- [BabyBabelLM: A Multilingual Benchmark of Developmentally Plausible Training Data](https://mlsanity.com/paper/2510.10159/) (2025)
- [From Unaligned to Aligned: Scaling Multilingual LLMs with Multi-Way Parallel Corpora](https://mlsanity.com/paper/2505.14045/) (2025)
- [Bringing Up a Bilingual BabyLM: Investigating Multilingual Language Acquisition Using Small-Scale Models](https://mlsanity.com/paper/2603.29552/) (2026)
- [Learning Bilingual Sentence Embeddings via Autoencoding and Computing Similarities with a Multilayer Perceptron](https://mlsanity.com/paper/1906.01942/) (2019)
- [Beyond Bilingual Transfer: Multilingual Code-Switching in Instruction Tuning](https://mlsanity.com/paper/2605.29414/) (2026)

## Abstract (arXiv)

During language acquisition, bilingual children are regularly exposed to code-switched input and use it as a cognitive scaffold to accelerate vocabulary growth and cross-linguistic syntactic mapping. In contrast, computational bilingual models are conventionally pretrained on interleaved monolingual corpora. While introducing synthetic code-switching during pretraining has become a promising strategy to enhance cross-lingual alignment and downstream performance, the structural and developmental parameters governing the success remain poorly understood. In this work, we investigate the efficiency of training with synthetic code-switched data across two typologically distinct language pairs by controlling two key variables: the structural location of code-switches and the dynamic switching rate across training stages. Our results show that training with code-switched data improves cross-lingual alignment for typologically close languages.