跳到正文
研踪PaperTrail
← 研踪 PaperTrail

arXiv:2611.01219 · v1

The Brittle Compass: Navigating LLM Prompt Sensitivity in Slovak Migration Media Discourse

首次提交 2026-11-01 03:41
版本更新 2026-11-01 03:41
雷达观测 2026-11-04 23:43
arXiv ↗PDF ↗

摘要

In this work, we present a case study that explores various tasks centered around the topic of migration in Slovak, a low-resource language, such as topic relevance and geographical relevance classification, and migration source/destination location term extraction. Our results demonstrate that native (Slovak) prompts yield a modest, task-dependent gains, while large models show significant robustness to prompt variations compared to their smaller counterparts. Analysis reveals that instructions (system or task) emerge as the most critical prompt component, more so than the examples sections, with task-specific performance benefits being more pronounced than overall language effects.

作者备注:14 pages, 16 figures. Published in the Proceedings of the First Workshop on Advancing NLP for Low-Resource Languages (LowResNLP 2025), associated with RANLP 2025, Varna, Bulgaria

期刊信息:Proceedings of the First Workshop on Advancing NLP for Low-Resource Languages, INCOMA Ltd., 2025, pages 88-101

AI 论文解读

基于摘要 · 非全文审阅

此版本尚未生成 AI 解读。解读由后台任务生成并按论文版本保存,登录后共享已有结果。

版本差异

目前只观测到一个版本,无法比较变化。之后收录新版本时,会在这里对照标题、作者、分类和摘要。

观测到的版本

相邻记录可能跳过中间版本;首次发现 v2 及以上也会标为版本更新。

v1 · 2026-11-01 03:41 · 最新观测版本

The Brittle Compass: Navigating LLM Prompt Sensitivity in Slovak Migration Media Discourse

In this work, we present a case study that explores various tasks centered around the topic of migration in Slovak, a low-resource language, such as topic relevance and geographical relevance classification, and migration source/destination location term extraction. Our results demonstrate that native (Slovak) prompts yield a modest, task-dependent gains, while large models show significant robustness to prompt variations compared to their smaller counterparts. Analysis reveals that instructions (system or task) emerge as the most critical prompt component, more so than the examples sections, with task-specific performance benefits being more pronounced than overall language effects.

查看此版本解读 →查看此版本原文 ↗