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<p>Personalized dialogue systems represent an innovative application in the field of conversational AI, aiming to endow chatbots with distinct personas to address the lack of individuality and specificity in traditional human-computer inter- actions.Current approaches often fail to incorporate rich external knowledge, making it difficult to maintain coherence and depth in long-term personal- ized interactions.Thus, to bridge the gap between static persona design and dynamic, knowledge-enhanced personalized dialogue generation, we did the fol- lowing work:1) We propose a novel Knowledge-expanded Personalized Dialogue Generation (KPDG) model to extend predefined personas using a commonsense knowledge graph of related personas. During the decoding of generated responses, this method adaptively integrates the most relevant personas that are optimally selected and partitioned with the dialogue history. 2) We design a two-stage prompting approach that leverages large language models (LLMs) for personal- ized dialogue generation. In the first stage, LLMs are used to enhance and expand the persona, enriching the persona’s intrinsic characteristics and emotional state. All in all, the main contributions of this work are as follows: (a) identification of the key limitations of current persona-based dialogue systems and formulation of a knowledge-enhanced framework to address them; (b) a novel persona expan- sion approach that combines structured common sense knowledge with adaptive selection; and (c) a two-stage LLM prompting paradigm that achieves state- of-the-art results without fine-tuning. Experiments on the PERSONA-CHAT dataset demonstrate that our approach outperforms strong baselines in both. 1. automatic metrics and human evaluations, validating its effectiveness in enhanc- ing persona diversity, contextual consistency, and conversational engagement. The novel contribution of this work is that we propose the KPDG framework, which first employs a knowledge graph–driven persona expansion module to enrich persona attributes and adaptively select context-relevant traits during decoding. Then, a two-stage LLM prompting strategy is applied: the first stage enhances and diversifies persona characteristics, while the second stage gener- ates responses via ICL without model retraining. This design not only addresses persona sparsity and consistency issues but also provides a scalable solution adaptable to different dialogue settings.</p>

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Personalized dialogue generation through knowledge expansion and in-context learning

  • 期刊论文

  • 发表日期：

    2025年12月16日

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  • Zhewen Wang,
  • Tinghuai Ma,
  • Huan Rong,
  • Li Jia

摘要

Personalized dialogue systems represent an innovative application in the field of conversational AI, aiming to endow chatbots with distinct personas to address the lack of individuality and specificity in traditional human-computer inter- actions.Current approaches often fail to incorporate rich external knowledge, making it difficult to maintain coherence and depth in long-term personal- ized interactions.Thus, to bridge the gap between static persona design and dynamic, knowledge-enhanced personalized dialogue generation, we did the fol- lowing work:1) We propose a novel Knowledge-expanded Personalized Dialogue Generation (KPDG) model to extend predefined personas using a commonsense knowledge graph of related personas. During the decoding of generated responses, this method adaptively integrates the most relevant personas that are optimally selected and partitioned with the dialogue history. 2) We design a two-stage prompting approach that leverages large language models (LLMs) for personal- ized dialogue generation. In the first stage, LLMs are used to enhance and expand the persona, enriching the persona’s intrinsic characteristics and emotional state. All in all, the main contributions of this work are as follows: (a) identification of the key limitations of current persona-based dialogue systems and formulation of a knowledge-enhanced framework to address them; (b) a novel persona expan- sion approach that combines structured common sense knowledge with adaptive selection; and (c) a two-stage LLM prompting paradigm that achieves state- of-the-art results without fine-tuning. Experiments on the PERSONA-CHAT dataset demonstrate that our approach outperforms strong baselines in both. 1. automatic metrics and human evaluations, validating its effectiveness in enhanc- ing persona diversity, contextual consistency, and conversational engagement. The novel contribution of this work is that we propose the KPDG framework, which first employs a knowledge graph–driven persona expansion module to enrich persona attributes and adaptively select context-relevant traits during decoding. Then, a two-stage LLM prompting strategy is applied: the first stage enhances and diversifies persona characteristics, while the second stage gener- ates responses via ICL without model retraining. This design not only addresses persona sparsity and consistency issues but also provides a scalable solution adaptable to different dialogue settings.