AgentCTG: Harnessing Multi-Agent Collaboration for Fine-Grained Precise Control in Text Generation

• URL: http://arxiv.org/abs/2509.13677v1
• Date: Wed, 17 Sep 2025 04:07:22 GMT
• Title: AgentCTG: Harnessing Multi-Agent Collaboration for Fine-Grained Precise Control in Text Generation
• Authors: Xinxu Zhou, Jiaqi Bai, Zhenqi Sun, Fanxiang Zeng, Yue Liu,
• Abstract summary: Controlled Text Generation (CTG) continues to face numerous challenges, particularly in achieving fine-grained conditional control over generation.<n>This paper introduces a novel and scalable framework, AgentCTG, which aims to enhance precise and complex control over the text generation.<n>AgentCTG achieves state-of-the-art results on multiple public datasets.
• Score: 10.208001921870709
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although significant progress has been made in many tasks within the field of Natural Language Processing (NLP), Controlled Text Generation (CTG) continues to face numerous challenges, particularly in achieving fine-grained conditional control over generation. Additionally, in real scenario and online applications, cost considerations, scalability, domain knowledge learning and more precise control are required, presenting more challenge for CTG. This paper introduces a novel and scalable framework, AgentCTG, which aims to enhance precise and complex control over the text generation by simulating the control and regulation mechanisms in multi-agent workflows. We explore various collaboration methods among different agents and introduce an auto-prompt module to further enhance the generation effectiveness. AgentCTG achieves state-of-the-art results on multiple public datasets. To validate its effectiveness in practical applications, we propose a new challenging Character-Driven Rewriting task, which aims to convert the original text into new text that conform to specific character profiles and simultaneously preserve the domain knowledge. When applied to online navigation with role-playing, our approach significantly enhances the driving experience through improved content delivery. By optimizing the generation of contextually relevant text, we enable a more immersive interaction within online communities, fostering greater personalization and user engagement.

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