Do LLMs Judge Distantly Supervised Named Entity Labels Well? Constructing the JudgeWEL Dataset

TL;DR

This paper introduces judgeWEL, a large, automatically generated Luxembourgish NER dataset using Wikipedia, Wikidata, and LLMs to improve resource scarcity issues in low-resource languages.

Contribution

The paper presents a novel pipeline combining structured data and LLM verification to create a large, high-quality NER dataset for Luxembourgish, addressing resource scarcity.

Findings

The dataset is five times larger than existing Luxembourgish NER datasets.

Using LLMs improves the quality of automatically annotated data.

The approach broadens entity coverage and reduces annotation noise.

Abstract

We present judgeWEL, a dataset for named entity recognition (NER) in Luxembourgish, automatically labelled and subsequently verified using large language models (LLM) in a novel pipeline. Building datasets for under-represented languages remains one of the major bottlenecks in natural language processing, where the scarcity of resources and linguistic particularities make large-scale annotation costly and potentially inconsistent. To address these challenges, we propose and evaluate a novel approach that leverages Wikipedia and Wikidata as structured sources of weak supervision. By exploiting internal links within Wikipedia articles, we infer entity types based on their corresponding Wikidata entries, thereby generating initial annotations with minimal human intervention. Because such links are not uniformly reliable, we mitigate noise by employing and comparing several LLMs to identify and retain only high-quality labelled sentences. The resulting corpus is approximately five times larger than the currently available Luxembourgish NER dataset and offers broader and more balanced coverage across entity categories, providing a substantial new resource for multilingual and low-resource NER research.