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        <title>System Report for CCL25-Eval Task 2 Solving Frame Semantic Parsing with LLMs</title>
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    <abstract>“Frame Semantic Parsing (FSP) is a critical task in natural language processing (NLP) that involves identifying semantic frames, argument spans, and their corresponding roles within a sentence. This paper presents a novel approach to Chinese Frame Seman-tic Parsing by fine-tuning the Qwen3 large language model to simultaneously address three sub-tasks: Frame Identification, Argument Identification, and Role Identification.We propose a unified prompt-based framework with iterative refinements, including direct argument output for span identification and a majority-voting mechanism for frame prediction. Our experiments demonstrate significant improvements in argument and role identification through modified output formats, while frame identification benefits from ensemble voting. However, integrating Chain-of-Thought (CoT) reasoning with model-generated explanations yielded suboptimal results, suggesting limitations in the auxiliary model’s performance. This work highlights the potential of fine-tuned large language models for complex semantic parsing tasks and identifies avenues for further optimization.”</abstract>
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%0 Conference Proceedings
%T System Report for CCL25-Eval Task 2 Solving Frame Semantic Parsing with LLMs
%A Dujingtao, Dujingtao
%Y Lin, Hongfei
%Y Li, Bin
%Y Tan, Hongye
%S Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025)
%D 2025
%8 August
%I Chinese Information Processing Society of China
%C Jinan, China
%F dujingtao-2025-system
%X “Frame Semantic Parsing (FSP) is a critical task in natural language processing (NLP) that involves identifying semantic frames, argument spans, and their corresponding roles within a sentence. This paper presents a novel approach to Chinese Frame Seman-tic Parsing by fine-tuning the Qwen3 large language model to simultaneously address three sub-tasks: Frame Identification, Argument Identification, and Role Identification.We propose a unified prompt-based framework with iterative refinements, including direct argument output for span identification and a majority-voting mechanism for frame prediction. Our experiments demonstrate significant improvements in argument and role identification through modified output formats, while frame identification benefits from ensemble voting. However, integrating Chain-of-Thought (CoT) reasoning with model-generated explanations yielded suboptimal results, suggesting limitations in the auxiliary model’s performance. This work highlights the potential of fine-tuned large language models for complex semantic parsing tasks and identifies avenues for further optimization.”
%U https://aclanthology.org/2025.ccl-2.8/
%P 70-75

Markdown (Informal)

[System Report for CCL25-Eval Task 2 Solving Frame Semantic Parsing with LLMs](https://aclanthology.org/2025.ccl-2.8/) (Dujingtao, CCL 2025)

• System Report for CCL25-Eval Task 2 Solving Frame Semantic Parsing with LLMs (Dujingtao, CCL 2025)

ACL

• Dujingtao Dujingtao. 2025. System Report for CCL25-Eval Task 2 Solving Frame Semantic Parsing with LLMs. In Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025), pages 70–75, Jinan, China. Chinese Information Processing Society of China.