MORPHEUS : Modeling Role from Personalized Dialogue History by Exploring and Utilizing Latent Space (2024)

Abstract: Personalized Dialogue Generation (PDG) aims to create coherent responses according to roles or personas. Traditional PDG relies on external role data, which can be scarce and raise privacy concerns. Approaches address these issues by extracting role information from dialogue history, which often fail to generically model roles in continuous space. To overcome these limitations, we introduce a novel framework Models Roles from Personalized Dialogue History by Exploring and Utilizing Latent Space (MORPHEUS) through a three-stage training process. Specifically, we create a persona codebook to represent roles in latent space compactly, and this codebook is used to construct a posterior distribution of role information. This method enables the model to generalize across roles, allowing the generation of personalized dialogues even for unseen roles. Experiments on both Chinese and English datasets demonstrate that MORPHEUS enhances the extraction of role information, and improves response generation without external role data. Additionally, MORPHEUS can be considered an efficient fine-tuning for large language models.

## introduction personalized dialogue generation @xcite (pdg), a critical method for capturing and responding to users' emotions @xcite and preferences @xcite in dialogues, aims to model role bases according to given roles or personas, thus generating role-relevant responses to meet users' individual needs. many methods have achieved commendable results by explicitly despite their successes, existing pdg methods overly rely on extracting information from sparse external role knowledge bases @xcite @xcite @xcite ; or brief textual persona descriptions @xcite @xcite . however, such external role data is often lacking and may raise privacy concerns. therefore, researchers attempt to leverage the role information presented in dialogue history @xcite @xcite . the msp @xcite introduces a tokenlevel retrieval-generation system that retrieves relevant personalized dialogues from a corpus and extracts tokens as prompts for generating responses. while msp employs a retrieval strategy for the implicit modeling of roles, it treats different roles in isolation and overlooks the inherent associations across roles. as illustrated in figure 1 , these models must grasp the concept that, similar to role a, role b derives comfort and enjoyment from "nature" and certain aspects of personal work and life, such as listening to music and spending time with pets, and utilize this understanding to craft appropriate responses, even when the persona data of role b is masked. to address this issue, clv @xcite ) attempts to model roles in latent space, dealing with similar characteristics through implicit clustering. furthermore, personapkl @xcite trains the model to perceive roles and then transfers this role perception ability to specific roles. however, these methods focus only on the associations between local role features, which leads to the model's difficulty in generating personalized responses for unseen roles. in fact, by learning dialogues based on roles, models could potentially acquire a universal ability to model unseen roles. to achieve this insight, we introduce mor-pheus, a framework that models roles from personalized dialogue history by exploring and utilizing latent space. first, we train the model to perceive roles, where it is supervised to generate personalized responses based on personas data and history, thereby establishing the relationship between persona information and dialogue history. next, we create a persona codebook to represent roles compactly in latent space, initializing it with the encoded representations of existing personas. finally, we construct a posterior distribution of persona information in the codebook using explicit personas and predict these codes using the dialogue history. ultimately, our three-stage training method results in a codebook that generalizes the representation of all roles. during inference, morpheus can automatically infer codes from the dialogue history, extract the code representations from the codebook, and use them to generate dialogue. in summary, our contributions are as follows: ## related work personalized dialogue generation previous research has demonstrated that incorporating personality traits into dialogue generation is crucial @xcite for personalizing dialogue systems. the question of how to maximize the utilization of explicit persona data when available is a concern for most studies. these data include factual role descriptions @xcite @xcite (e.g., "i enjoy swimming"), personality traits (e.g., introverted), knowledge bases @xcite @xcite @xcite (e.g., gender: male), and speaking styles @xcite (e.g., excited). these data contain rich persona information, enabling dialogue systems to efficiently and reliably extract information for use in dialogues. in a word, the goal of all research is to model a role profile with personality, engaging in personalized, continuous dialogue with users. however, compared to explicit data, which is scarce and difficult to extract from the real world, dialogue histories can be collected on a large scale and also contain rich persona information. modeling personality solely from dialogue history is challenging but offers significant advantages, including avoiding privacy issues associated with explicit data. the essence of modeling personalities from dialogue history is to extract and utilize generic persona information. some past studies, such as dhap @xcite and msp @xcite , enhance current dialogue generation by explicitly retrieving relevant dialogues from the entire corpus. these approaches achieve the goal of modeling roles from dialogue history but suffer from the limitations of corpus size and the inefficiency of retrieval-based methods. unlike retrieve-based studies, such as personawae @xcite , model personalization in the latent space for the user side without considering the personality of the respondent. personalpkt @xcite , achieve the goal of inferring dialogue for target roles without explicit data by efficiently fine-tuning the model in two stages with explicit persona data, but this still requires additional training for each target. clv @xcite argues that explicit persona descriptions hide certain specific angle information. it clusters persona information in a latent variable space and obtains information solely from dialogue history through a cave @xcite . furthermore, we believe that all persona information can actually be compressed in a larger latent space for generalization, which can then be utilized during dialogue generation. ## further analysis further analyses are based on the convai2 dataset, and similar phenomena can be observed on baidu personachat. ## conclusion in this work, we introduce the morpheus framework for generating personalized responses. unlike existing efforts, we model roles in a generalized manner from the dialogue history of roles and align roles with dialogues through a multi-stage training process to enhance the personalization of dialogue generation. experimental results demonstrate that our model is capable of generating highly personalized responses even when faced with previously unseen roles, efficiently utilizing persona information while mitigating the risk of privacy breaches.