# Towards Proactive Information Probing: Customer Service Chatbots Harvesting Value from Conversation

arXiv: https://arxiv.org/abs/2604.11077 · HTML: https://mlsanity.com/paper/2604.11077/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 49 references that are in the map.
- First posted on arXiv 13 Apr 2026.

## Builds on (most cited first, 18 of 18)

- [FinQA: A Dataset of Numerical Reasoning over Financial Data](https://mlsanity.com/paper/2109.00122/) (2021)
- [MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents](https://mlsanity.com/paper/2404.10774/) (2024)
- [Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback](https://mlsanity.com/paper/2305.10142/) (2023)
- [Multi-hop Reading Comprehension through Question Decomposition and Rescoring](https://mlsanity.com/paper/1906.02916/) (2019)
- [A Survey on Proactive Dialogue Systems: Problems, Methods, and Prospects](https://mlsanity.com/paper/2305.02750/) (2023)
- [Towards Human-centered Proactive Conversational Agents](https://mlsanity.com/paper/2404.12670/) (2024)
- [INSCIT: Information-Seeking Conversations with Mixed-Initiative Interactions](https://mlsanity.com/paper/2207.00746/) (2022)
- [Supernotes: Driving Consensus in Crowd-Sourced Fact-Checking](https://mlsanity.com/paper/2411.06116/) (2024)
- [Multiple Choice Questions based Multi-Interest Policy Learning for Conversational Recommendation](https://mlsanity.com/paper/2112.11775/) (2021)
- [STYLE: Improving Domain Transferability of Asking Clarification Questions in Large Language Model Powered Conversational Agents](https://mlsanity.com/paper/2405.12059/) (2024)
- [Dial-In LLM: Human-Aligned LLM-in-the-loop Intent Clustering for Customer Service Dialogues](https://mlsanity.com/paper/2412.09049/) (2024)
- [Chatbots for Data Collection in Surveys: A Comparison of Four Theory-Based Interview Probes](https://mlsanity.com/paper/2503.08582/) (2025)
- [Natural Language Processing in Customer Service: A Systematic Review](https://mlsanity.com/paper/2212.09523/) (2022)
- [Empowering Air Travelers: A Chatbot for Canadian Air Passenger Rights](https://mlsanity.com/paper/2403.12678/) (2024)
- [Evaluating Empathetic Chatbots in Customer Service Settings](https://mlsanity.com/paper/2101.01334/) (2021)
- [One System to Rule them All: a Universal Intent Recognition System for Customer Service Chatbots](https://mlsanity.com/paper/2112.08261/) (2021)
- [Linguistic Elements of Engaging Customer Service Discourse on Social Media](https://mlsanity.com/paper/2212.12801/) (2022)
- [AI Chatbots as Professional Service Agents: Developing a Professional Identity](https://mlsanity.com/paper/2501.14179/) (2025)

## Built on by (most cited first, 1 of 1)

- [Clarify User Expertise: Towards Proactive Conversational Agents Tailoring Responses to User Proficiency](https://mlsanity.com/paper/2608.22266/) (2026)

## Abstract (arXiv)

Customer service chatbots are increasingly expected to serve not merely as reactive support tools for users, but as strategic interfaces for harvesting high-value information and business intelligence. In response, we make three main contributions. 1) We introduce and define a novel task of Proactive Information Probing, which optimizes when to probe users for pre-specified target information while minimizing conversation turns and user friction. 2) We propose PROCHATIP, a proactive chatbot framework featuring a specialized conversation strategy module trained to master the delicate timing of probes. 3) Experiments demonstrate that PROCHATIP significantly outperforms baselines, exhibiting superior capability in both information probing and service quality. We believe that our work effectively redefines the commercial utility of chatbots, positioning them as scalable, cost-effective engines for proactive business intelligence. Our code is available at https://github.com/SCUNLP/PROCHATIP.