# LLM-Guided Lifecycle-Aware Clustering of Multi-Turn Customer Support Conversations

arXiv: https://arxiv.org/abs/2601.04388 · HTML: https://mlsanity.com/paper/2601.04388/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 40 references that are in the map.
- First posted on arXiv 7 Jan 2026.

## Builds on (most cited first, 16 of 16)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://mlsanity.com/paper/2104.08663/) (2021)
- [Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models](https://mlsanity.com/paper/2108.08877/) (2021)
- [Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning](https://mlsanity.com/paper/2011.01403/) (2020)
- [Survey of Large Multimodal Model Datasets, Application Categories and Taxonomy](https://mlsanity.com/paper/2412.17759/) (2024)
- [SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use](https://mlsanity.com/paper/2505.17332/) (2025)
- [Hard Negative Mining for Domain-Specific Retrieval in Enterprise Systems](https://mlsanity.com/paper/2505.18366/) (2025)
- [LLM for Barcodes: Generating Diverse Synthetic Data for Identity Documents](https://mlsanity.com/paper/2411.14962/) (2024)
- [Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts](https://mlsanity.com/paper/2412.03590/) (2024)
- [Tokenization Matters: Improving Zero-Shot NER for Indic Languages](https://mlsanity.com/paper/2504.16977/) (2025)
- [Clinical QA 2.0: Multi-Task Learning for Answer Extraction and Categorization](https://mlsanity.com/paper/2502.13108/) (2025)
- [Hybrid AI for Responsive Multi-Turn Online Conversations with Novel Dynamic Routing and Feedback Adaptation](https://mlsanity.com/paper/2506.02097/) (2025)
- [Multi-turn Dialogue Comprehension from a Topic-aware Perspective](https://mlsanity.com/paper/2309.09666/) (2023)
- [Review of Tools for Zero-Code LLM Based Application Development](https://mlsanity.com/paper/2510.19747/) (2025)

## Built on by (most cited first, 2 of 2)

- [IndicJR: A Judge-Free Benchmark of Jailbreak Robustness in South Asian Languages](https://mlsanity.com/paper/2602.16832/) (2026)
- [RAILS: Retrieval-Augmented Incremental LLM Clustering at Scale](https://mlsanity.com/paper/2609.24464/) (2026)

## Abstract (arXiv)

Clustering customer chat data is vital for cloud providers handling multi service queries. Traditional methods struggle with overlapping concerns and create broad, static clusters that degrade over time. Reclustering disrupts continuity, making issue tracking difficult. We propose an adaptive system that segments multi turn chats into service specific concerns and incrementally refines clusters as new issues arise. Cluster quality is tracked via DaviesBouldin Index and Silhouette Scores, with LLM based splitting applied only to degraded clusters. Our method improves Silhouette Scores by over 100% and reduces DBI by 65.6% compared to baselines, enabling scalable, real time analytics without full reclustering.