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Active Learning with Large Language Models for Cross-lingual Pseudo-labeling in Computational Social Science

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Active Learning with Large Language Models for Cross-lingual Pseudo-labeling in Computational Social Science Large Language Models (LLMs) have become ubiquitous in the area of Natural Language Processing (NLP). This project aims to advance the applications of LLMs in the multidisciplinary field of Computational Social Science (CSS). Specifically, LLMs are often used to pseud-label (at scale) variables in data that are of interest to social scientists (e.g., a psychologist would look for emotion markers, an economist for statements about money, a human rights researcher for toxic language etc.) I will develop and test novel tooling for applying LLMs to efficiently pseudo-label CSS data in a cross-lingual setting. The project will proceed in three phases. First, a dataset and methodology for evaluating the language robustness of LLMs (isolated from all other confounding factors) will be developed. Second, u

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recordType

award

status

SIGNED

region

EU

value

214344.72

unit

EUR