IP-Dialog: Evaluating Implicit Personalization in Dialogue Systems with Synthetic Data

• URL: http://arxiv.org/abs/2506.02449v1
• Date: Tue, 03 Jun 2025 05:14:11 GMT
• Title: IP-Dialog: Evaluating Implicit Personalization in Dialogue Systems with Synthetic Data
• Authors: Bo Peng, Zhiheng Wang, Heyang Gong, Chaochao Lu,
• Abstract summary: In modern dialogue systems, the ability to implicitly infer user backgrounds from conversations is crucial.<n>Traditional dataset construction methods are labor-intensive, resource-demanding, and raise privacy concerns.<n>We propose a novel approach for automatic synthetic data generation and introduce the Implicit Personalized Dialogue benchmark.
• Score: 7.1268134621069805
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In modern dialogue systems, the ability to implicitly infer user backgrounds from conversations and leverage this information for personalized assistance is crucial. However, the scarcity of high-quality data remains a fundamental challenge to evaluating and improving this capability. Traditional dataset construction methods are labor-intensive, resource-demanding, and raise privacy concerns. To address these issues, we propose a novel approach for automatic synthetic data generation and introduce the Implicit Personalized Dialogue (IP-Dialog) benchmark along with a training dataset, covering 10 tasks and 12 user attribute types. Additionally, we develop a systematic evaluation framework with four metrics to assess both attribute awareness and reasoning capabilities. We further propose five causal graphs to elucidate models' reasoning pathways during implicit personalization. Extensive experiments yield insightful observations and prove the reliability of our dataset.

Related papers

• IMPersona: Evaluating Individual Level LM Impersonation [28.040025302581366]
  We introduce IMPersona, a framework for evaluating LMs at impersonating specific individuals' writing style and personal knowledge.<n>We demonstrate that even modestly sized open-source models, such as Llama-3.1-8B-Instruct, can achieve impersonation abilities at concerning levels.
  arXiv  Detail & Related papers  (2025-04-06T02:57:58Z)
• PersonaBench: Evaluating AI Models on Understanding Personal Information through Accessing (Synthetic) Private User Data [76.21047984886273]
  Personalization is critical in AI assistants, particularly in the context of private AI models that work with individual users.<n>Due to the sensitive nature of such data, there are no publicly available datasets that allow us to assess an AI model's ability to understand users.<n>We introduce a synthetic data generation pipeline that creates diverse, realistic user profiles and private documents simulating human activities.
  arXiv  Detail & Related papers  (2025-02-28T00:43:35Z)
• Dialogue Language Model with Large-Scale Persona Data Engineering [10.160626284195434]
  PPDS is an open-domain persona dialogue system that employs extensive generative pre-training on a persona dialogue dataset to enhance persona consistency.<n>We present a persona extraction model designed to autonomously and precisely generate vast persona dialogue datasets.<n>We also unveil a pioneering persona augmentation technique to address the invalid persona bias inherent in the constructed dataset.
  arXiv  Detail & Related papers  (2024-12-12T07:49:06Z)
• CI-Bench: Benchmarking Contextual Integrity of AI Assistants on Synthetic Data [7.357348564300953]
  CI-Bench is a comprehensive benchmark for evaluating the ability of AI assistants to protect personal information during model inference. We present a novel, scalable, multi-step data pipeline for generating natural communications, including dialogues and emails. We formulate and evaluate a naive AI assistant to demonstrate the need for further study and careful training towards personal assistant tasks.
  arXiv  Detail & Related papers  (2024-09-20T21:14:36Z)
• PersLLM: A Personified Training Approach for Large Language Models [66.16513246245401]
  We propose PersLLM, a framework for better data construction and model tuning.<n>For insufficient data usage, we incorporate strategies such as Chain-of-Thought prompting and anti-induction.<n>For rigid behavior patterns, we design the tuning process and introduce automated DPO to enhance the specificity and dynamism of the models' personalities.
  arXiv  Detail & Related papers  (2024-07-17T08:13:22Z)
• Recent Trends in Personalized Dialogue Generation: A Review of Datasets, Methodologies, and Evaluations [25.115319934091282]
  This paper seeks to survey the recent landscape of personalized dialogue generation. Covering 22 datasets, we highlight benchmark datasets and newer ones enriched with additional features. We analyze 17 seminal works from top conferences between 2021-2023 and identify five distinct types of problems.
  arXiv  Detail & Related papers  (2024-05-28T09:04:13Z)
• Dataset Regeneration for Sequential Recommendation [69.93516846106701]
  We propose a data-centric paradigm for developing an ideal training dataset using a model-agnostic dataset regeneration framework called DR4SR. To demonstrate the effectiveness of the data-centric paradigm, we integrate our framework with various model-centric methods and observe significant performance improvements across four widely adopted datasets.
  arXiv  Detail & Related papers  (2024-05-28T03:45:34Z)
• AUGUST: an Automatic Generation Understudy for Synthesizing Conversational Recommendation Datasets [56.052803235932686]
  We propose a novel automatic dataset synthesis approach that can generate both large-scale and high-quality recommendation dialogues. In doing so, we exploit: (i) rich personalized user profiles from traditional recommendation datasets, (ii) rich external knowledge from knowledge graphs, and (iii) the conversation ability contained in human-to-human conversational recommendation datasets.
  arXiv  Detail & Related papers  (2023-06-16T05:27:14Z)
• FCC: Fusing Conversation History and Candidate Provenance for Contextual Response Ranking in Dialogue Systems [53.89014188309486]
  We present a flexible neural framework that can integrate contextual information from multiple channels. We evaluate our model on the MSDialog dataset widely used for evaluating conversational response ranking tasks.
  arXiv  Detail & Related papers  (2023-03-31T23:58:28Z)
• Enhancing Dialogue Generation via Multi-Level Contrastive Learning [57.005432249952406]
  We propose a multi-level contrastive learning paradigm to model the fine-grained quality of the responses with respect to the query. A Rank-aware (RC) network is designed to construct the multi-level contrastive optimization objectives. We build a Knowledge Inference (KI) component to capture the keyword knowledge from the reference during training and exploit such information to encourage the generation of informative words.
  arXiv  Detail & Related papers  (2020-09-19T02:41:04Z)

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.