@inproceedings{pan-wu-2025-scunlp,
title = "{SCUNLP} at {ROCLING}-2025 Shared Task: Systematic Guideline Refinement for Continuous Value Prediction with Outlier-Driven {LLM} Feedback",
author = "Pan, Hong Rui and
Wu, Jheng Long",
editor = "Chang, Kai-Wei and
Lu, Ke-Han and
Yang, Chih-Kai and
Tam, Zhi-Rui and
Chang, Wen-Yu and
Wang, Chung-Che",
booktitle = "Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025)",
month = nov,
year = "2025",
address = "National Taiwan University, Taipei City, Taiwan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.rocling-main.47/",
pages = "418--426",
ISBN = "979-8-89176-379-1",
abstract = "Regression-based prediction is widely applied to continuous outputs, such as emotion dimension estimation. However, traditional methods struggle to handle unclear annotation standards and ambiguous cases. To address this challenge, we propose a dual-layer agent-executor framework, where the agent is responsible for constructing and refining guidelines, while the executor applies these guidelines to annotate large-scale data. Notably, we introduce a novel refinement mechanism that can detect outlier instances and provide feedback to the agent for guideline revision, thereby achieving iterative improvement. We applied this method to the ROCLING 2025 shared task for predicting valence-arousal (VA) values in medical self-reflection texts. Compared to the unmodified version, the outlier-driven configuration effectively reduced MAE for both V/A, with A-MAE significantly decreased by 7.7{\%}. The final valence-MAE was 0.51 and arousal-MAE was 0.87, ranking fourth."
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%0 Conference Proceedings
%T SCUNLP at ROCLING-2025 Shared Task: Systematic Guideline Refinement for Continuous Value Prediction with Outlier-Driven LLM Feedback
%A Pan, Hong Rui
%A Wu, Jheng Long
%Y Chang, Kai-Wei
%Y Lu, Ke-Han
%Y Yang, Chih-Kai
%Y Tam, Zhi-Rui
%Y Chang, Wen-Yu
%Y Wang, Chung-Che
%S Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025)
%D 2025
%8 November
%I Association for Computational Linguistics
%C National Taiwan University, Taipei City, Taiwan
%@ 979-8-89176-379-1
%F pan-wu-2025-scunlp
%X Regression-based prediction is widely applied to continuous outputs, such as emotion dimension estimation. However, traditional methods struggle to handle unclear annotation standards and ambiguous cases. To address this challenge, we propose a dual-layer agent-executor framework, where the agent is responsible for constructing and refining guidelines, while the executor applies these guidelines to annotate large-scale data. Notably, we introduce a novel refinement mechanism that can detect outlier instances and provide feedback to the agent for guideline revision, thereby achieving iterative improvement. We applied this method to the ROCLING 2025 shared task for predicting valence-arousal (VA) values in medical self-reflection texts. Compared to the unmodified version, the outlier-driven configuration effectively reduced MAE for both V/A, with A-MAE significantly decreased by 7.7%. The final valence-MAE was 0.51 and arousal-MAE was 0.87, ranking fourth.
%U https://aclanthology.org/2025.rocling-main.47/
%P 418-426
Markdown (Informal)
[SCUNLP at ROCLING-2025 Shared Task: Systematic Guideline Refinement for Continuous Value Prediction with Outlier-Driven LLM Feedback](https://aclanthology.org/2025.rocling-main.47/) (Pan & Wu, ROCLING 2025)
ACL