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

• URL: http://arxiv.org/abs/2407.02345v1
• Date: Tue, 2 Jul 2024 15:12:34 GMT
• Title: MORPHEUS: Modeling Role from Personalized Dialogue History by Exploring and Utilizing Latent Space
• Authors: Yihong Tang, Bo Wang, Dongming Zhao, Xiaojia Jin, Jijun Zhang, Ruifang He, Yuexian Hou,
• Abstract summary: Traditional Personalized Dialogue Generation (PDG) relies on external role data, which can be scarce and raise privacy concerns. We introduce a novel framework textbfMOdels textbfRoles from textbfPersonalized Dialogue textbfHistory by textbfExploring and textbfUtilizing Latent textbfSpace (MORPHEUS) through a three-stage training process.
• Score: 16.508815826492814
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 \textbf{MO}dels \textbf{R}oles from \textbf{P}ersonalized Dialogue \textbf{H}istory by \textbf{E}xploring and \textbf{U}tilizing Latent \textbf{S}pace (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.

Related papers

• ERABAL: Enhancing Role-Playing Agents through Boundary-Aware Learning [17.5855800570993]
  Role-playing is an emerging application in the field of Human-Computer Interaction (HCI) Despite significant progress, role-playing agents (RPLAs) still struggle with maintaining role-consistency across conversations. We present ERABAL, a framework aimed at enhancing RPLAs' role-playing capabilities through boundary-aware learning.
  arXiv  Detail & Related papers  (2024-09-23T05:12:13Z)
• BEYOND DIALOGUE: A Profile-Dialogue Alignment Framework Towards General Role-Playing Language Model [12.617285298415013]
  The rapid advancement of large language models (LLMs) has revolutionized role-playing, enabling the development of general role-playing models. Current role-playing training has two significant issues: (I) Using a predefined role profile to prompt dialogue training for specific scenarios leads to inconsistencies and even conflicts between the dialogue and the profile, resulting in training biases. We propose a simple yet effective framework called DIALOGUE, designed to overcome these hurdles.
  arXiv  Detail & Related papers  (2024-08-20T14:47:38Z)
• The Oscars of AI Theater: A Survey on Role-Playing with Language Models [38.68597594794648]
  This survey explores the burgeoning field of role-playing with language models. It focuses on their development from early persona-based models to advanced character-driven simulations facilitated by Large Language Models (LLMs) We provide a comprehensive taxonomy of the critical components in designing these systems, including data, models and alignment, agent architecture and evaluation.
  arXiv  Detail & Related papers  (2024-07-16T08:20:39Z)
• Capturing Minds, Not Just Words: Enhancing Role-Playing Language Models with Personality-Indicative Data [58.92110996840019]
  We propose to enhance role-playing language models (RPLMs) via personality-indicative data. Specifically, we leverage questions from psychological scales and distill advanced RPAs to generate dialogues that grasp the minds of characters. Experimental results validate that RPLMs trained with our dataset exhibit advanced role-playing capabilities for both general and personality-related evaluations.
  arXiv  Detail & Related papers  (2024-06-27T06:24:00Z)
• Learning Retrieval Augmentation for Personalized Dialogue Generation [29.467644429517325]
  This paper studies the potential of leveraging external knowledge for persona dialogue generation. Experiments conducted on the CONVAI2 dataset with ROCStory as a supplementary data source show that the proposed LAPDOG method substantially outperforms the baselines.
  arXiv  Detail & Related papers  (2024-06-27T02:38:13Z)
• Prompt Framework for Role-playing: Generation and Evaluation [3.2845546753303867]
  Large language models (LLMs) exhibit impressive proficiency in natural language generation, understanding user instructions, and emulating human-like language use. This project introduces a prompt-based framework designed to leverage GPT's capabilities for the generation of role-playing dialogue datasets.
  arXiv  Detail & Related papers  (2024-06-02T06:09:56Z)
• Large Language Models are Superpositions of All Characters: Attaining Arbitrary Role-play via Self-Alignment [62.898963074989766]
  We introduce Ditto, a self-alignment method for role-play. This method creates a role-play training set comprising 4,000 characters, surpassing the scale of currently available datasets by tenfold. We present the first comprehensive cross-supervision alignment experiment in the role-play domain.
  arXiv  Detail & Related papers  (2024-01-23T03:56:22Z)
• Large Language Models Meet Harry Potter: A Bilingual Dataset for Aligning Dialogue Agents with Characters [70.84938803753062]
  We introduce the Harry Potter Dialogue dataset, designed to advance the study of dialogue agents and character alignment. The dataset encompasses all dialogue sessions (in both English and Chinese) from the Harry Potter series. It is annotated with vital background information, including dialogue scenes, speakers, character relationships, and attributes.
  arXiv  Detail & Related papers  (2022-11-13T10:16:39Z)
• Unsupervised Neural Stylistic Text Generation using Transfer learning and Adapters [66.17039929803933]
  We propose a novel transfer learning framework which updates only $0.3%$ of model parameters to learn style specific attributes for response generation. We learn style specific attributes from the PERSONALITY-CAPTIONS dataset.
  arXiv  Detail & Related papers  (2022-10-07T00:09:22Z)
• Manual-Guided Dialogue for Flexible Conversational Agents [84.46598430403886]
  How to build and use dialogue data efficiently, and how to deploy models in different domains at scale can be critical issues in building a task-oriented dialogue system. We propose a novel manual-guided dialogue scheme, where the agent learns the tasks from both dialogue and manuals. Our proposed scheme reduces the dependence of dialogue models on fine-grained domain ontology, and makes them more flexible to adapt to various domains.
  arXiv  Detail & Related papers  (2022-08-16T08:21:12Z)
• Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation [70.81596088969378]
  Cross-lingual Outline-based Dialogue dataset (termed COD) enables natural language understanding. COD enables dialogue state tracking, and end-to-end dialogue modelling and evaluation in 4 diverse languages.
  arXiv  Detail & Related papers  (2022-01-31T18:11:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.