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        <title>Chinese Parataxis Graph(CPG) Parsing Based on Large Language Models</title>
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            <title>Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)</title>
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            <namePart type="given">Hongfei</namePart>
            <namePart type="family">Lin</namePart>
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    <abstract>“This paper presents the work submitted for the 23rd China National Conference on Computational Linguistics(Evaluation Workshop)(CCL24-Eval), focusing on the Chinese Parataxis Graph (CPG) Parsing task. CPG represents Chinese natural language hierarchically through relational triplets, providing a consistent representation for linguistic units of varying levels. Our approach has used large-scale language models through full fine-tuning, achieving the result with F1 value at 71.6% in the contest and 74.76% after the contest. Furtehrmore, our team has proposed a combined model that integrates multiple LoRA fine-tuned medium-scale models after the contest. This approach is able to minimize the time and space consumption while keeping the performance of CPG construction task relatively high.”</abstract>
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        <url>https://aclanthology.org/2024.ccl-3.6/</url>
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%0 Conference Proceedings
%T Chinese Parataxis Graph(CPG) Parsing Based on Large Language Models
%A YueYi, Sun
%A Yuxuan, Wang
%Y Lin, Hongfei
%Y Tan, Hongye
%Y Li, Bin
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G eng
%F yueyi-yuxuan-2024-chinese
%X “This paper presents the work submitted for the 23rd China National Conference on Computational Linguistics(Evaluation Workshop)(CCL24-Eval), focusing on the Chinese Parataxis Graph (CPG) Parsing task. CPG represents Chinese natural language hierarchically through relational triplets, providing a consistent representation for linguistic units of varying levels. Our approach has used large-scale language models through full fine-tuning, achieving the result with F1 value at 71.6% in the contest and 74.76% after the contest. Furtehrmore, our team has proposed a combined model that integrates multiple LoRA fine-tuned medium-scale models after the contest. This approach is able to minimize the time and space consumption while keeping the performance of CPG construction task relatively high.”
%U https://aclanthology.org/2024.ccl-3.6/
%P 51-61

Markdown (Informal)

[Chinese Parataxis Graph(CPG) Parsing Based on Large Language Models](https://aclanthology.org/2024.ccl-3.6/) (YueYi & Yuxuan, CCL 2024)

• Chinese Parataxis Graph(CPG) Parsing Based on Large Language Models (YueYi & Yuxuan, CCL 2024)

ACL

• Sun YueYi and Wang Yuxuan. 2024. Chinese Parataxis Graph(CPG) Parsing Based on Large Language Models. In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations), pages 51–61, Taiyuan, China. Chinese Information Processing Society of China.