Technology Category
Natural Language Processing: GenerationMachine Learning: Large Multimodal Models (LMMs)Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
Do Large Language Models Need Intent? Revisiting Response Generation Strategies for Service Assistant
🤖 AI Summary
This study investigates whether explicit intent recognition is a necessary prerequisite for generating high-quality responses in task-oriented dialogue systems. Method: Challenging the conventional intent-module-dependent paradigm, we propose and comparatively evaluate two strategies—“intent-first” and “end-to-end direct generation”—using large language models (e.g., T5) fine-tuned on multiple public task-oriented dialogue datasets. Evaluation encompasses both linguistic quality and task completion rate. Contribution/Results: Experiments demonstrate that, in typical service scenarios, direct generation achieves performance on par with or exceeding that of the intent-first approach—even without intent annotations—while substantially reducing system complexity and inference latency. These findings empirically challenge the assumed necessity of explicit intent recognition, providing evidence and conceptual support for lightweight, low-latency service assistant design grounded in a new end-to-end paradigm.
Technology Category
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Evaluating necessity of explicit intent recognition for service responses
Comparing Intent-First versus Direct Response Generation paradigms
Assessing linguistic quality and task success rates in conversational AI
Innovation
Methods, ideas, or system contributions that make the work stand out.
Direct Response Generation without explicit intent
Comparative study of Intent-First versus Direct paradigms
Benchmarking T5 models on service interaction datasets
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