# Can Large Language Models Understand, Reason About, and Generate Code-Switched Text?

arXiv: https://arxiv.org/abs/2601.07153 · HTML: https://mlsanity.com/paper/2601.07153/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 10 of its 34 references that are in the map.
- First posted on arXiv 12 Jan 2026.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 10 of 10)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025)
- [gpt-oss-120b & gpt-oss-20b Model Card](https://mlsanity.com/paper/2508.10925/) (2025)
- [Olmo 3](https://mlsanity.com/paper/2512.13961/) (2025)
- [SimpleQA Verified: A Reliable Factuality Benchmark to Measure Parametric Knowledge](https://mlsanity.com/paper/2509.07968/) (2025)
- [Crosslingual Reasoning through Test-Time Scaling](https://mlsanity.com/paper/2505.05408/) (2025)
- [Grammatical Constraints on Intra-sentential Code-Switching: From Theories to Working Models](https://mlsanity.com/paper/1612.04538/) (2016)
- [Linguistics Theory Meets LLM: Code-Switched Text Generation via Equivalence Constrained Large Language Models](https://mlsanity.com/paper/2410.22660/) (2024)
- [Beg to Differ: Understanding Reasoning-Answer Misalignment Across Languages](https://mlsanity.com/paper/2512.22712/) (2025)

## Built on by (most cited first, 5 of 5)

- [Bringing Up a Bilingual BabyLM: Investigating Multilingual Language Acquisition Using Small-Scale Models](https://mlsanity.com/paper/2603.29552/) (2026)
- [Code-Switching Reveals Language Anchoring in Multilingual LLMs](https://mlsanity.com/paper/2606.19668/) (2026)
- [When Surface Form Changes Moderation Decisions: A Paired Study of Code-Mixed Workflow Instability](https://mlsanity.com/paper/2606.05654/) (2026)
- [SpeechJBB: Probing Safety Alignment and Comprehension in Large Audio Language Models under Code-Switched Speech](https://mlsanity.com/paper/2606.06037/) (2026)
- [CSWBench: Evaluating Context-Sensitive Code-Switching in Creative Writing](https://mlsanity.com/paper/2610.20272/) (2026)

## Abstract (arXiv)

Code-switching is a pervasive phenomenon in multilingual communication, yet the robustness of large language models (LLMs) in mixed-language settings remains insufficiently understood. In this work, we present a comprehensive evaluation of LLM capabilities in understanding, reasoning over, and generating code-switched text. We introduce CodeMixQA a novel benchmark with high-quality human annotations, comprising 16 diverse parallel code-switched language-pair variants that span multiple geographic regions and code-switching patterns, and include both original scripts and their transliterated forms. Using this benchmark, we analyze the reasoning behavior of LLMs on code-switched question-answering tasks, shedding light on how models process and reason over mixed-language inputs. We further conduct a systematic evaluation of LLM-generated synthetic code-switched text, focusing on both naturalness and semantic fidelity, and uncover key limitations in current generation capabilities. Our findings reveal persistent challenges in both reasoning and generation under code-switching conditions and provide actionable insights for building more robust multilingual LLMs. We release the dataset and code as open source.