LLM Memory & RAG 相关度: 9/10

AIANO: Enhancing Information Retrieval with AI-Augmented Annotation

Sameh Khattab, Marie Bauer, Lukas Heine, Till Rostalski, Jens Kleesiek, Julian Friedrich
arXiv: 2602.04579v1 发布: 2026-02-04 更新: 2026-02-04

AI 摘要

AIANO通过AI辅助标注,显著提升了信息检索数据集的创建效率和质量。

主要贡献

  • 开发了AIANO:一个AI辅助标注工具。
  • 提出了AI增强的标注流程,结合人工和LLM的优势。
  • 实验证明AIANO能显著提升标注速度、易用性和检索准确率。

方法论

采用AI辅助标注工作流,用户研究对比AIANO与基线工具在问答数据集创建上的性能,评估标注速度、易用性和检索准确率。

原文摘要

The rise of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) has rapidly increased the need for high-quality, curated information retrieval datasets. These datasets, however, are currently created with off-the-shelf annotation tools that make the annotation process complex and inefficient. To streamline this process, we developed a specialized annotation tool - AIANO. By adopting an AI-augmented annotation workflow that tightly integrates human expertise with LLM assistance, AIANO enables annotators to leverage AI suggestions while retaining full control over annotation decisions. In a within-subject user study ($n = 15$), participants created question-answering datasets using both a baseline tool and AIANO. AIANO nearly doubled annotation speed compared to the baseline while being easier to use and improving retrieval accuracy. These results demonstrate that AIANO's AI-augmented approach accelerates and enhances dataset creation for information retrieval tasks, advancing annotation capabilities in retrieval-intensive domains.

标签

信息检索 数据标注 LLM RAG AI辅助

arXiv 分类

cs.IR cs.CL