Conversational systems are crucial for human-computer interaction, managing
complex dialogues by identifying threads and prioritising responses. This is
especially vital in multi-party conversations, where precise identification of
threads and strategic response prioritisation ensure efficient dialogue
management. To address these challenges an end-to-end model that identifies
threads and prioritises their response generation based on the importance was
developed, involving a systematic decomposition of the problem into discrete
components - thread detection, prioritisation, and performance optimisation
which was meticulously analysed and optimised. These refined components
seamlessly integrate into a unified framework, in conversational systems.
Llama2 7b is used due to its high level of generalisation but the system can be
updated with any open source Large Language Model(LLM). The computational
capabilities of the Llama2 model was augmented by using fine tuning methods and
strategic prompting techniques to optimise the model's performance, reducing
computational time and increasing the accuracy of the model. The model achieves
up to 10x speed improvement, while generating more coherent results compared to
existing models.

通过对话管理中的线程检测、优先排序以及性能优化的系统分解和优化，开发了一种端到端模型，该模型能够基于重要性识别线程并优先生成其响应。此外，使用 Llama2 模型结合微调方法和策略提示技术，增强了计算能力，提高了模型速度和生成结果的连贯性。