大模型生成服务回复，是否必须先识别意图？
Do Large Language Models Need Intent? Revisiting Response Generation Strategies for Service Assistant
- 对比了先识别意图再生成和直接生成两种策略
- 直接生成在任务成功率上表现不输甚至更优
- 适合追求高效响应的智能客服系统设计
在对话式AI时代，生成准确且符合上下文的服务回复仍是关键挑战。核心问题在于：生成高质量服务回复是否必须依赖显式的意图识别？还是模型可跳过此步骤，直接生成有效回复？本文通过严谨的对比研究回答这一基础设计难题。基于两个公开的服务交互数据集，我们对多种顶尖语言模型（包括微调后的T5变体）在两种范式下进行了基准测试：先意图后生成，以及直接生成。评估指标涵盖语言质量与任务成功率，揭示了关于显式意图建模必要性的意外发现。研究结果挑战了对话式AI流水线中的传统假设，为设计更高效、更有效的回复生成系统提供了切实可行的指导。
原文摘要 · Abstract (English)
In the era of conversational AI, generating accurate and contextually appropriate service responses remains a critical challenge. A central question remains: Is explicit intent recognition a prerequisite for generating high-quality service responses, or can models bypass this step and produce effective replies directly? This paper conducts a rigorous comparative study to address this fundamental design dilemma. Leveraging two publicly available service interaction datasets, we benchmark several state-of-the-art language models, including a fine-tuned T5 variant, across both paradigms: Intent-First Response Generation and Direct Response Generation. Evaluation metrics encompass both linguistic quality and task success rates, revealing surprising insights into the necessity or redundancy of explicit intent modelling. Our findings challenge conventional assumptions in conversational AI pipelines, offering actionable guidelines for designing more efficient and effective response generation systems.
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