# Target-oriented Proactive Dialogue Systems with Personalization: Problem Formulation and Dataset Curation

arXiv: https://arxiv.org/abs/2310.07397 · HTML: https://mlsanity.com/paper/2310.07397/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 41 references that are in the map.
- First posted on arXiv 11 Oct 2023.

## Builds on (most cited first, 22 of 22)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection](https://mlsanity.com/paper/2301.07597/) (2023)
- [Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation](https://mlsanity.com/paper/1911.03842/) (2019)
- [Towards Topic-Guided Conversational Recommender System](https://mlsanity.com/paper/2010.04125/) (2020)
- [Proactive Human-Machine Conversation with Explicit Conversation Goals](https://mlsanity.com/paper/1906.05572/) (2019)
- [ACUTE-EVAL: Improved Dialogue Evaluation with Optimized Questions and Multi-turn Comparisons](https://mlsanity.com/paper/1909.03087/) (2019)
- [Target-Guided Open-Domain Conversation](https://mlsanity.com/paper/1905.11553/) (2019)
- [A Survey on Proactive Dialogue Systems: Problems, Methods, and Prospects](https://mlsanity.com/paper/2305.02750/) (2023)
- [Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generation](https://mlsanity.com/paper/2204.08128/) (2022)
- [DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation](https://mlsanity.com/paper/2109.08877/) (2021)
- [OTTers: One-turn Topic Transitions for Open-Domain Dialogue](https://mlsanity.com/paper/2105.13710/) (2021)
- [Keyword-Guided Neural Conversational Model](https://mlsanity.com/paper/2012.08383/) (2020)
- [Dynamic Knowledge Routing Network For Target-Guided Open-Domain Conversation](https://mlsanity.com/paper/2002.01196/) (2020)
- [Controlling Personality-Based Stylistic Variation with Neural Natural Language Generators](https://mlsanity.com/paper/1805.08352/) (2018)
- [Dialogue Planning via Brownian Bridge Stochastic Process for Goal-directed Proactive Dialogue](https://mlsanity.com/paper/2305.05290/) (2023)
- [BotsTalk: Machine-sourced Framework for Automatic Curation of Large-scale Multi-skill Dialogue Datasets](https://mlsanity.com/paper/2210.12687/) (2022)
- [Target-Guided Dialogue Response Generation Using Commonsense and Data Augmentation](https://mlsanity.com/paper/2205.09314/) (2022)
- [You Truly Understand What I Need: Intellectual and Friendly Dialogue Agents grounding Knowledge and Persona](https://mlsanity.com/paper/2301.02401/) (2023)
- [Identifying Personality Traits Using Overlap Dynamics in Multiparty Dialogue](https://mlsanity.com/paper/1909.00876/) (2019)

## Built on by (most cited first, 23 of 23)

- [A Survey of Personalization: From RAG to Agent](https://mlsanity.com/paper/2504.10147/) (2025)
- [A Survey on Recent Advances in Conversational Data Generation](https://mlsanity.com/paper/2405.13003/) (2024)
- [Training Turn-by-Turn Verifiers for Dialogue Tutoring Agents: The Curious Case of LLMs as Your Coding Tutors](https://mlsanity.com/paper/2502.13311/) (2025)
- [Zero-shot Persuasive Chatbots with LLM-Generated Strategies and Information Retrieval](https://mlsanity.com/paper/2407.03585/) (2024)
- [ChatSOP: An SOP-Guided MCTS Planning Framework for Controllable LLM Dialogue Agents](https://mlsanity.com/paper/2407.03884/) (2024)
- [Instruct Once, Chat Consistently in Multiple Rounds: An Efficient Tuning Framework for Dialogue](https://mlsanity.com/paper/2402.06967/) (2024)
- [The Imperative of Conversation Analysis in the Era of LLMs: A Survey of Tasks, Techniques, and Trends](https://mlsanity.com/paper/2409.14195/) (2024)
- [CarMem: Enhancing Long-Term Memory in LLM Voice Assistants through Category-Bounding](https://mlsanity.com/paper/2501.09645/) (2025)
- [ProactiveEval: A Unified Evaluation Framework for Proactive Dialogue Agents](https://mlsanity.com/paper/2508.20973/) (2025)
- [Multi-Party Conversational Agents: A Survey](https://mlsanity.com/paper/2505.18845/) (2025)
- [AutoPal: Autonomous Adaptation to Users for Personal AI Companionship](https://mlsanity.com/paper/2406.13960/) (2024)
- [Towards Personalized Conversational Sales Agents: Contextual User Profiling for Strategic Action](https://mlsanity.com/paper/2504.08754/) (2025)
- [ConsistentChat: Building Skeleton-Guided Consistent Multi-Turn Dialogues for Large Language Models from Scratch](https://mlsanity.com/paper/2506.03558/) (2025)
- [Data Selection for Multi-turn Dialogue Instruction Tuning](https://mlsanity.com/paper/2604.07892/) (2026)
- [PEToolLLM: Towards Personalized Tool Learning in Large Language Models](https://mlsanity.com/paper/2502.18980/) (2025)
- [Enhancing Goal-oriented Proactive Dialogue Systems via Consistency Reflection and Correction](https://mlsanity.com/paper/2506.13366/) (2025)
- [Proactive Service Agents: A Unified Decision Framework, Methods, and Evaluation](https://mlsanity.com/paper/2609.03727/) (2026)
- [IP-Dialog: Evaluating Implicit Personalization in Dialogue Systems with Synthetic Data](https://mlsanity.com/paper/2506.02449/) (2025)
- [A Concise Agent is Less Expert: Revealing Side Effects of Using Style Features on Conversational Agents](https://mlsanity.com/paper/2601.10809/) (2026)
- [Planning with Diffusion Models for Target-Oriented Dialogue Systems](https://mlsanity.com/paper/2504.16858/) (2025)
- [SimRPD: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selection](https://mlsanity.com/paper/2601.02871/) (2026)
- [Beyond Model Scaling: Test-Time Intervention for Efficient Deep Reasoning](https://mlsanity.com/paper/2601.11252/) (2026)
- [ATOD: An Evaluation Framework and Benchmark for Agentic Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2601.11854/) (2026)

## Abstract (arXiv)

Target-oriented dialogue systems, designed to proactively steer conversations toward predefined targets or accomplish specific system-side goals, are an exciting area in conversational AI. In this work, by formulating a <dialogue act, topic> pair as the conversation target, we explore a novel problem of personalized target-oriented dialogue by considering personalization during the target accomplishment process. However, there remains an emergent need for high-quality datasets, and building one from scratch requires tremendous human effort. To address this, we propose an automatic dataset curation framework using a role-playing approach. Based on this framework, we construct a large-scale personalized target-oriented dialogue dataset, TopDial, which comprises about 18K multi-turn dialogues. The experimental results show that this dataset is of high quality and could contribute to exploring personalized target-oriented dialogue.