{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/miracle-towards-personalized-dialogue","title":"MIRACLE: Towards Personalized Dialogue Generation with Latent-Space Multiple Personal Attribute Control","arxiv_id":"2310.18342","date":"2023-10-22","proceeding":null,"authors":["Zhenyi Lu","Wei Wei","Xiaoye Qu","Xianling Mao","Dangyang Chen","Jixiong Chen"],"abstract":"Personalized dialogue systems aim to endow the chatbot agent with more anthropomorphic traits for human-like interactions. Previous approaches have explored explicitly user profile modeling using text descriptions, implicit derivation of user embeddings, or utilizing handicraft prompts for ChatGPT-like models. However, textual personas are limited in describing multi-faceted attributes (\\emph{e.g.}, \\emph{language style, inner character nuances}), implicit embedding suffers from personality sparsity, and handicraft prompts lack fine-grained and stable controllability. Hence, these approaches may struggle with complex personalized dialogue generation tasks that require generating controllable responses with multiple personal attributes. To this end, we propose \\textbf{\\textsc{Miracle}}, a novel personalized dialogue generation method through \\textbf{M}ult\\textbf{I}ple Pe\\textbf{R}sonal \\textbf{A}ttributes \\textbf{C}ontrol within \\textbf{L}atent-Space \\textbf{E}nergy-based Models. ttributes \\textbf{C}ontrol within \\textbf{L}atent-Space \\textbf{E}nergy-based Models. Specifically, our approach first disentangles complex personality into multi-faceted attributes. Subsequently, we employ a conditional variational auto-encoder to align with the dense personalized responses within a latent joint attribute space. We have also tailored a dedicated energy function and customized the ordinary differential equations sampling method to offer flexible attribute composition and precise attribute control. Extensive experiments demonstrate that \\textsc{Miracle} outperforms several strong baselines in terms of personality controllability and response generation quality. Our dataset and code are available at \\url{https://github.com/LZY-the-boys/MIRACLE}","url_abs":"https://arxiv.org/abs/2310.18342v1","url_pdf":"https://arxiv.org/pdf/2310.18342v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"miracle-towards-personalized-dialogue","repo_url":"https://github.com/lzy-the-boys/miracle","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"chatbot","task_name":"Chatbot"},{"task_slug":"dialogue-generation","task_name":"Dialogue Generation"},{"task_slug":"response-generation","task_name":"Response Generation"}],"methods":[{"method_slug":"align","method_name":"ALIGN"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2310.18342","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}