根据模型熵动态调整控制强度，让对话生成更可控又自然。
ECO Decoding: Entropy-Based Control for Controllability and Fluency in Controllable Dialogue Generation
- 用模型熵自动调节每步的控制力度，避免固定参数弊端。
- 在DailyDialog和MultiWOZ上同时提升可控性与流畅性。
- 适合需要精准控制对话风格或属性的生成任务。
可控对话生成（CDG）使聊天机器人能生成具备特定属性的回复，加权解码方法已取得显著成效。然而，使用固定常数调节属性概率偏差，难以找到兼顾可控性与流畅性的理想控制强度。为此，本文提出基于熵的控制方法（ECO decoding），根据语言模型和属性分类器概率分布的熵，在每一步动态调整控制强度。在DailyDialog和MultiWOZ数据集上的实验表明，ECO解码持续提升可控性，同时保持流畅性和语法正确性，优于多种先前解码方法。此外，该方法缓解了多属性生成中的概率插值问题，在单属性和多属性场景中均表现优异。
原文摘要 · Abstract (English)
Controllable Dialogue Generation (CDG) enables chatbots to generate responses with desired attributes, and weighted decoding methods have achieved significant success in the CDG task. However, using a fixed constant value to manage the bias of attribute probabilities makes it challenging to find an ideal control strength that satisfies both controllability and fluency. To address this issue, we propose ECO decoding (Entropy-based COntrol), which dynamically adjusts the control strength at each generation step according to the model's entropy in both the language model and attribute classifier probability distributions. Experiments on the DailyDialog and MultiWOZ datasets demonstrate that ECO decoding consistently improves controllability while maintaining fluency and grammaticality, outperforming prior decoding methods across various models and settings. Furthermore, ECO decoding alleviates probability interpolation issues in multi-attribute generation and consequently demonstrates strong performance in both single and multi-attribute scenarios.
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