Year: 2026
Type: conference-paper
Abstract: Research on ancient Chinese language is of great significance for tracing Chinese history and civilization.In the field of large language models, studies on the pre-Qin excavated documents such as Oracle Bone Inscriptions, Bronze Inscriptions, and Bamboo Book of Chu remain insufficient.This is because these ancient characters have a low level of digitization, training corpora are extremely scarce, and they typically contain complex and rich semantic information.Therefore, we propose an ancient character semantic-aware embedding for large language models.This embedding integrates both the glyph and lexicality of ancient characters and maps them to the modern Chinese semantic space.We also design a two-stage method for lightweight and parameter-efficient training of the embedding.Finally, we conduct extensive experiments on excavated documents from the pre-Qin period, and the results demonstrate the effectiveness of our approach.
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166799277