What's Happening?
A recent study has focused on automating the compilation of a dictionary of philosophical terms from Pre-Qin thinkers using large language models (LLMs). The study outlines a technical workflow consisting of three modules: corpus and dataset construction,
pretraining and fine-tuning strategies, and dictionary construction and visualization. The corpus includes primary source materials from authoritative digital platforms, encompassing 36 classical texts from various Pre-Qin philosophical traditions. The study also integrates annotated commentaries from the Han to Qing dynasties to provide semantic scaffolding for model training. The fine-tuning dataset is based on the Encyclopedia of Chinese Philosophy, containing 430 data triples of philosophical terms, affiliated schools, and textual instances. The study employs a systematic training paradigm using the LLaMA Factory framework, incorporating Low-Rank Adaptation techniques for efficient fine-tuning. The final component involves structured dictionary compilation and interactive visualization, allowing for dynamic querying and browsing of dictionary content.
Why It's Important?
This development is significant as it leverages advanced AI technologies to preserve and enhance the understanding of ancient philosophical texts. By automating the compilation of a philosophical lexicon, the study not only aids in the preservation of cultural heritage but also enhances accessibility to these texts for scholars and researchers. The use of LLMs in this context demonstrates the potential of AI to handle complex linguistic tasks, such as semantic disambiguation and contextual interpretation, which are crucial for understanding philosophical discourse. This approach could set a precedent for similar projects in other fields of the humanities, where large volumes of historical texts require detailed analysis and interpretation.
