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Title: Enhancing Multilingual LLMs with Code-Switching

Speaker: Haneul Yoo @ NYU

Time : 11:00 - 12:00, June 1st, 2026

Location: 

Online: https://hli.skku.edu/InvitedTalk260601 

In-person: 85777

Language: English speech & English slides

Abstract: 

Large language models (LLMs) have advanced rapidly, but they are significantly underdeveloped for languages other than English. In this talk, we present three recent studies that leverage code-switching to enhance and evaluate multilingual LLMs. We begin with Code-Switching Red-Teaming (CSRT), a multilingual adversarial evaluation framework that reveals LLMs are significantly more vulnerable to harmful behavior when prompted with code-switched inputs—particularly involving low-resource languages—highlighting an unintended correlation between language resource availability and safety alignment. To address these challenges, the second part introduces Code-Switching Curriculum Learning (CSCL), a training strategy inspired by human language acquisition that progressively fine-tunes LLMs using intra- and inter-sentential code-switching and monolingual data. CSCL improves cross-lingual alignment, performance in low-resource languages, and safety robustness, demonstrating that code-switching serves not only as an effective probing lens but also as a practical tool for equitable multilingual adaptation. We finally present Code-Switching In-Context Learning (CSICL), an inference-time technique that facilitates the latent translation process of LLMs via code-switching and improves their multilingual reasoning.

Bio:

Haneul Yoo is an incoming Assistant Professor/Faculty Fellow in the Department of Computer Science at the Courant Institute of Mathematical Sciences, New York University. She will receive her PhD from the School of Computing at KAIST in August 2026. Previously, she was a visiting scholar at NYU and a lecturer at Boostcamp AI Tech. She has also interned at NAVER AI Lab, Upstage, KEPCO Research Institute, and CSIRO. With a background in machine learning (ML) and natural language processing (NLP), she has worked on (1) developing language models and methods to improve generalization across languages, (2) designing resources and evaluation frameworks for underrepresented languages and cultures, and (3) applying AI to high-stakes domains. She has published papers in major NLP conferences, including ACL, EMNLP, and NAACL, and has been named a Rising Star at KAIST exploreCSR supported by Google. She has co-organized the Multilingual and Equitable Language Technologies (MELT) workshop at COLM 2025, the Women in Machine Learning (WiML) symposium at ICML 2026, and the Generative AI for the World (GenAI4World) workshop at COLM 2026.