  Classical Chinese is a gateway to the rich heritage and wisdom of ancient
China, yet its complexities pose formidable comprehension barriers for most
modern people without specialized knowledge. While Large Language Models (LLMs)
have shown remarkable capabilities in Natural Language Processing (NLP), they
struggle with Classical Chinese Understanding (CCU), especially in
data-demanding and knowledge-intensive tasks. In response to this dilemma, we
propose \textbf{TongGu} (mean understanding ancient and modern), the first
CCU-specific LLM, underpinned by three core contributions. First, we construct
a two-stage instruction-tuning dataset ACCN-INS derived from rich classical
Chinese corpora, aiming to unlock the full CCU potential of LLMs. Second, we
propose Redundancy-Aware Tuning (RAT) to prevent catastrophic forgetting,
enabling TongGu to acquire new capabilities while preserving its foundational
knowledge. Third, we present a CCU Retrieval-Augmented Generation (CCU-RAG)
technique to reduce hallucinations based on knowledge-grounding. Extensive
experiments across 24 diverse CCU tasks validate TongGu's superior ability,
underscoring the effectiveness of RAT and CCU-RAG. The model and dataset will
be public available.
