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| Open-domain dialogue systems are designed to fulfill people\'s daily communicative and emotional requirements, with the goal of cultivating long-term relationships with users. Yet, these systems encounter challenges in sustaining persona consistency, as responses generated at times are not logically aligned with the established character persona or preceding dialogues. This discrepancy undermines dialogue coherence and emotional engagement, consequently impeding the development of profound connections with users. Addressing this issue, this study introduce an innovative dialogue generation algorithm that incorporates retrieval-augmentation techniques. By forming an database of character information, the algorithm aids large language models in retrieving persona-relevant data during interactions, ensuring that responses consistently align with the character\'s defined persona. This method significantly mitigates the occurrence of generating inconsistent response, an "hallucination" effect. This study demonstrates the substantial impact of information optimization and filtering mechanisms on enhancing persona consistency within dialogue systems, as evidenced through comprehensive evaluation across three pivotal performance metrics: information relevance, faithfulness, and reponse relevance, facilitated by an integration of various retrieval strategies and information optimization techniques. |
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| Keywords:Computer science; Persona Consistency; Dialogue Generation; Large Language Models |
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