Efficient and Human-Inspired Natural Language Processing Methods for Multilingual and Low-Resource Settings

MCML Authors

Ercong Nie

Dr.

* Former Member

→ Group Hinrich Schütze
 Computational Linguistics

Abstract

This dissertation advances multilingual NLP for low-resource languages by developing prompt-based, retrieval-augmented, and parameter-efficient methods that improve zero- and few-shot performance across diverse languages and tasks. It also combines linguistic, cognitive, and mechanistic analyses to better understand and mitigate multilingual weaknesses in LLMs, contributing to more inclusive, robust, and interpretable language technologies. (Shortened.)

phdthesis Nie25

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Dissertation

LMU München. Oct. 2025

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Authors

E. Nie

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Links

DOI

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Research Area

 B2 | Natural Language Processing