@inproceedings{lu-etal-2026-chairo,
title = "{CHAIRO}: Contextual Hierarchical Analogical Induction and Reasoning Optimization for {LLM}s",
author = "Lu, Haotian and
Mou, Yuchen and
Wu, Bingzhe",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1692/",
pages = "36526--36537",
ISBN = "979-8-89176-390-6",
abstract = "Warning: This paper may contain content that could be disturbing or offensive. Content moderation in online platforms faces persistent challenges due to the evolving complexity of user-generated content and the limitations of traditional rule-based and machine learning approaches. While recent advances in large language models (LLMs) have enabled more sophisticated moderation via direct prompting or fine-tuning, these approaches often exhibit limited generalization, interpretability, and adaptability to unseen or ambiguous cases.In this work, we propose a novel moderation framework that leverages analogical examples to enhance rule induction and decision reliability. Our approach integrates end-to-end optimization of analogical retrieval, rule generation, and moderation classification, enabling the dynamic adaptation of moderation rules to diverse content scenarios. Through comprehensive experiments, we demonstrate that our method significantly outperforms both rule-injected fine-tuning baselines and multi-stage static RAG pipelines in terms of moderation accuracy and rule quality. Further evaluations{---}including human assessments and external model generalization tests confirm the superiority of rules generated by our framework in terms of clarity, interpretability, and applicability. These findings highlight the potential of analogical example-driven methods for advancing robust, explainable, and generalizable content moderation in real-world applications."
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<abstract>Warning: This paper may contain content that could be disturbing or offensive. Content moderation in online platforms faces persistent challenges due to the evolving complexity of user-generated content and the limitations of traditional rule-based and machine learning approaches. While recent advances in large language models (LLMs) have enabled more sophisticated moderation via direct prompting or fine-tuning, these approaches often exhibit limited generalization, interpretability, and adaptability to unseen or ambiguous cases.In this work, we propose a novel moderation framework that leverages analogical examples to enhance rule induction and decision reliability. Our approach integrates end-to-end optimization of analogical retrieval, rule generation, and moderation classification, enabling the dynamic adaptation of moderation rules to diverse content scenarios. Through comprehensive experiments, we demonstrate that our method significantly outperforms both rule-injected fine-tuning baselines and multi-stage static RAG pipelines in terms of moderation accuracy and rule quality. Further evaluations—including human assessments and external model generalization tests confirm the superiority of rules generated by our framework in terms of clarity, interpretability, and applicability. These findings highlight the potential of analogical example-driven methods for advancing robust, explainable, and generalizable content moderation in real-world applications.</abstract>
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%0 Conference Proceedings
%T CHAIRO: Contextual Hierarchical Analogical Induction and Reasoning Optimization for LLMs
%A Lu, Haotian
%A Mou, Yuchen
%A Wu, Bingzhe
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F lu-etal-2026-chairo
%X Warning: This paper may contain content that could be disturbing or offensive. Content moderation in online platforms faces persistent challenges due to the evolving complexity of user-generated content and the limitations of traditional rule-based and machine learning approaches. While recent advances in large language models (LLMs) have enabled more sophisticated moderation via direct prompting or fine-tuning, these approaches often exhibit limited generalization, interpretability, and adaptability to unseen or ambiguous cases.In this work, we propose a novel moderation framework that leverages analogical examples to enhance rule induction and decision reliability. Our approach integrates end-to-end optimization of analogical retrieval, rule generation, and moderation classification, enabling the dynamic adaptation of moderation rules to diverse content scenarios. Through comprehensive experiments, we demonstrate that our method significantly outperforms both rule-injected fine-tuning baselines and multi-stage static RAG pipelines in terms of moderation accuracy and rule quality. Further evaluations—including human assessments and external model generalization tests confirm the superiority of rules generated by our framework in terms of clarity, interpretability, and applicability. These findings highlight the potential of analogical example-driven methods for advancing robust, explainable, and generalizable content moderation in real-world applications.
%U https://aclanthology.org/2026.acl-long.1692/
%P 36526-36537
Markdown (Informal)
[CHAIRO: Contextual Hierarchical Analogical Induction and Reasoning Optimization for LLMs](https://aclanthology.org/2026.acl-long.1692/) (Lu et al., ACL 2026)
ACL