The advent of Large Language Models (LLMs) has led to remarkable progress on
a wide range of natural language processing tasks. Despite the advances, these
large-sized models still suffer from hallucinating information in their output,
which poses a major issue in automatic text summarization, as we must guarantee
that the generated summary is consistent with the content of the source
document. Previous research addresses the challenging task of detecting
hallucinations in the output (i.e. inconsistency detection) in order to
evaluate the faithfulness of the generated summaries. However, these works
primarily focus on English and recent multilingual approaches lack German data.
This work presents absinth, a manually annotated dataset for hallucination
detection in German news summarization and explores the capabilities of novel
open-source LLMs on this task in both fine-tuning and in-context learning
settings. We open-source and release the absinth dataset to foster further
research on hallucination detection in German.

本研究介绍了一个用于德语新闻摘要中幻觉检测的手动标注数据集，并探索了新型开源大语言模型在该任务的微调和上下文学习设置中的能力。