基于大小语言模型协同的社区矛盾调解框架OA

The large language models(LLM)are increasingly being applied in people's lives due to their remarkable ability in contextual learning and causal reasoning.Corresponding to the capabilities of LLM,mediators in community conflict resolution require a certain level of discernment and a neutral perspective to mediate conflicts after understanding them thoroughly.Therefore,LLM can to some extent alleviate the issues of insufficient human resources,high mediation difficulty,and lack of credibility in existing community conflict resolution systems.However,the cost of invoking LLM limits their usage in communities.This paper proposes a framework based on the collaboration between large and small language models.The framework utilizes freely available small language models to generate conflict summaries and is divided into two approaches:human-machine diversion and human-machine collaboration,based on the mediator's level of involvement.Experiments demonstrate that this framework can reduce the cost while approaching the mediation quality provided by human mediators.