# A Diverse and Effective Retrieval-Based Debt Collection System with Expert Knowledge

arXiv: https://arxiv.org/abs/2504.06273 · HTML: https://mlsanity.com/paper/2504.06273/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 30 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://mlsanity.com/paper/2403.13372/) (2024)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems](https://mlsanity.com/paper/1506.08909/) (2015)
- [Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots](https://mlsanity.com/paper/1612.01627/) (2016)
- [Modeling Multi-turn Conversation with Deep Utterance Aggregation](https://mlsanity.com/paper/1806.09102/) (2018)
- [Learning an Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues](https://mlsanity.com/paper/2009.06265/) (2020)
- [Multi-turn Response Selection using Dialogue Dependency Relations](https://mlsanity.com/paper/2010.01502/) (2020)
- [DDRel: A New Dataset for Interpersonal Relation Classification in Dyadic Dialogues](https://mlsanity.com/paper/2012.02553/) (2020)
- [Matching Questions and Answers in Dialogues from Online Forums](https://mlsanity.com/paper/2005.09276/) (2020)
- [Toward An Optimal Selection of Dialogue Strategies: A Target-Driven Approach for Intelligent Outbound Robots](https://mlsanity.com/paper/2206.10953/) (2022)
- [Two-Level Supervised Contrastive Learning for Response Selection in Multi-Turn Dialogue](https://mlsanity.com/paper/2203.00793/) (2022)
- [Semantic Space Grounded Weighted Decoding for Multi-Attribute Controllable Dialogue Generation](https://mlsanity.com/paper/2305.02820/) (2023)
- [Dial-MAE: ConTextual Masked Auto-Encoder for Retrieval-based Dialogue Systems](https://mlsanity.com/paper/2306.04357/) (2023)

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

- [Following the Flow the Law Requires: Training Voice Agents to Transfer to Unseen Regulated Procedures in Debt Collection](https://mlsanity.com/paper/2610.18881/) (2026)

## Abstract (arXiv)

Designing effective debt collection systems is crucial for improving operational efficiency and reducing costs in the financial industry. However, the challenges of maintaining script diversity, contextual relevance, and coherence make this task particularly difficult. This paper presents a debt collection system based on real debtor-collector data from a major commercial bank. We construct a script library from real-world debt collection conversations, and propose a two-stage retrieval based response system for contextual relevance. Experimental results show that our system improves script diversity, enhances response relevance, and achieves practical deployment efficiency through knowledge distillation. This work offers a scalable and automated solution, providing valuable insights for advancing debt collection practices in real-world applications.