Decoding Nested Entities from Classical Chinese with LLMs.
Year: 2025
Type: article
Abstract: This repository contains the datasets, source codes, and experimental results for the study “Decoding Nested Entities from Classical Chinese with LLMs.” The research focuses on extracting and analyzing nested entities from large-scale Chinese local gazetteers. We design a data sampling strategy integrated with the multi-objective optimization algorithm NSGA-II to address entity imbalance in historical low-resource corpora. Two Large Language Models, Qwen 2.5-7B and Xunzi-Qwen2-7B, are fine-tuned with LoRA and few-shot prompt learning, and compared with non-generative baselines (GlobalPointer, Biaffine, TPLinker).
Source: Figshare
Author: Xiaoci, Lin
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7089461269
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