# Doing Personal LAPS: LLM-Augmented Dialogue Construction for Personalized Multi-Session Conversational Search

arXiv: https://arxiv.org/abs/2405.03480 · HTML: https://mlsanity.com/paper/2405.03480/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2024.

## Built on by (most cited first, 24 of 24)

- [PersonaLens: A Benchmark for Personalization Evaluation in Conversational AI Assistants](https://mlsanity.com/paper/2506.09902/) (2025)
- [A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrieval](https://mlsanity.com/paper/2503.05659/) (2025)
- [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)
- [A Survey of Conversational Search](https://mlsanity.com/paper/2410.15576/) (2024)
- [AGENT-CQ: Automatic Generation and Evaluation of Clarifying Questions for Conversational Search with LLMs](https://mlsanity.com/paper/2410.19692/) (2024)
- [Generate then Refine: Data Augmentation for Zero-shot Intent Detection](https://mlsanity.com/paper/2410.01953/) (2024)
- [ToolSpectrum : Towards Personalized Tool Utilization for Large Language Models](https://mlsanity.com/paper/2505.13176/) (2025)
- [ConvMix: A Mixed-Criteria Data Augmentation Framework for Conversational Dense Retrieval](https://mlsanity.com/paper/2508.04001/) (2025)
- [PerPilot: Personalizing VLM-based Mobile Agents via Memory and Exploration](https://mlsanity.com/paper/2508.18040/) (2025)
- [Enabling Chatbots with Eyes and Ears: An Immersive Multimodal Conversation System for Dynamic Interactions](https://mlsanity.com/paper/2506.00421/) (2025)
- [MassTool: A Multi-Task Search-Based Tool Retrieval Framework for Large Language Models](https://mlsanity.com/paper/2507.00487/) (2025)
- [EventWeave: A Dynamic Framework for Capturing Core and Supporting Events in Dialogue Systems](https://mlsanity.com/paper/2503.23078/) (2025)
- [Accessibility Scout: Personalized Accessibility Scans of Built Environments](https://mlsanity.com/paper/2507.23190/) (2025)
- [REVISION:Reflective Intent Mining and Online Reasoning Auxiliary for E-commerce Visual Search System Optimization](https://mlsanity.com/paper/2510.22739/) (2025)
- [Is Passive Expertise-Based Personalization Enough? A Case Study in AI-Assisted Test-Taking](https://mlsanity.com/paper/2511.23376/) (2025)
- [TrajWiki: Source-Grounded Memory Trajectories for Long-Horizon Dialogue Agents](https://mlsanity.com/paper/2608.00967/) (2026)
- [From Profiling to Synthesis: Benchmarking Implicit Behavioral Alignment in Personalized LLM Agents](https://mlsanity.com/paper/2608.02171/) (2026)
- [Large Language Model Driven Recommendation](https://mlsanity.com/paper/2408.10946/) (2024)
- [Leveraging Graph Structures and Large Language Models for End-to-End Synthetic Task-Oriented Dialogues](https://mlsanity.com/paper/2501.11977/) (2025)
- [Dagstuhl Perspectives Workshop 24352 -- Conversational Agents: A Framework for Evaluation (CAFE): Manifesto](https://mlsanity.com/paper/2506.11112/) (2025)
- [PromptAug: Fine-grained Conflict Classification Using Data Augmentation](https://mlsanity.com/paper/2506.22491/) (2025)
- [Personalised Explanations in Long-term Human-Robot Interactions](https://mlsanity.com/paper/2507.03049/) (2025)
- [Interplay: Training Independent Simulators for Reference-Free Conversational Recommendation](https://mlsanity.com/paper/2603.18573/) (2026)

## Abstract (arXiv)

The future of conversational agents will provide users with personalized information responses. However, a significant challenge in developing models is the lack of large-scale dialogue datasets that span multiple sessions and reflect real-world user preferences. Previous approaches rely on experts in a wizard-of-oz setup that is difficult to scale, particularly for personalized tasks. Our method, LAPS, addresses this by using large language models (LLMs) to guide a single human worker in generating personalized dialogues. This method has proven to speed up the creation process and improve quality. LAPS can collect large-scale, human-written, multi-session, and multi-domain conversations, including extracting user preferences. When compared to existing datasets, LAPS-produced conversations are as natural and diverse as expert-created ones, which stays in contrast with fully synthetic methods. The collected dataset is suited to train preference extraction and personalized response generation. Our results show that responses generated explicitly using extracted preferences better match user's actual preferences, highlighting the value of using extracted preferences over simple dialogue history. Overall, LAPS introduces a new method to leverage LLMs to create realistic personalized conversational data more efficiently and effectively than previous methods.