DistilledGemma: Balanced Efficiency-Accuracy for Person-Place Relation Extraction from Multilingual Historical Articles
arXiv:2606.29130v1 Announce Type: new Abstract: We present DistilledGemma, an efficient and accurate system for the HIPE-2026 shared task on person-place relation extraction from multilingual historical newspaper articles in English, German, and French. Our approach adopts a three-stage knowledge distillation pipeline designed to balance classification accuracy with computational efficiency. In the first stage, we systematically explored prompt engineering strategies across eight large language models to identify the most effective reasoning architecture for this challenging task. In the secon
The continuous development in AI aims to optimize models for specific historical and multilingual natural language processing tasks, indicating ongoing refinement in AI capabilities.
This development highlights the push towards more efficient and accurate AI models for specialized applications, which has implications for data analysis, information retrieval, and historical research across various languages.
The introduction of DistilledGemma suggests an improved approach to balancing efficiency and accuracy in person-place relation extraction, particularly for multilingual historical documents, enabling more robust analysis in this domain.
- · Historians
- · Archivists
- · NLP researchers
- · Data scientists
- · Labor-intensive manual data annotation
- · Inefficient general-purpose AI models
- · Organizations relying on less accurate extraction methods
More accurate and faster extraction of entities and their relationships from historical texts will become possible.
Improved understanding and analysis of historical geopolitical and social patterns could emerge from large-scale data processing.
New research methodologies and tools based on efficient AI could accelerate historical linguistics and digital humanities across multiple languages.
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