Plan, Generate and Optimize: Extending Large Language Models for Dialogue Systems Via Prompt-Based Collaborativec Method

The advancements in large language models (LLMs) have significantly propelled the level of artificial intelligence, further enhancing the model’s problem-solving capabilities across a variety of dialogue-oriented tasks. However, the substantial costs associated with training and inference processes for LLMs hinder their deployment across various dialogue scenarios, while small language models (SLMs) tend to perform poorly with limited samples in new settings or domains. Therefore, we propose a collaborative mechanism between LLMs and SLMs, wherein prompts are employed to bridge the gap between them. For the dialogue system, the LLM acts as a source from which SLM derives, facilitating task planning, data generating, training and optimization. Experimental results indicate that our method can significantly reduce inference overhead in new dialogue scenarios and outperforms the original pipeline architecture in terms of inference performance.