Building end-to-end dialogue system with large language models

In order to solve the complex module dependencies of dialogue systems and improve the system's ability to understand deep knowledge in natural language and produce more coherent texts, this paper introduces an end-to-end dialogue system based on large language models. First, low-rank adaption is used to fine-tune sequence-to-sequence large language models, which reduces system complexity and model fine-tuning cost. Then, the training method of reinforcement learning from human feedback is adopted to make the generated responses more aligned with human expectations. Finally, in-context learning is used to adapt to specific tasks, improving model flexibility and adaptability. Experimental results show that the system performs well in both automatic evaluation and practical use and has strong application value.