# Optimized Algorithms for Text Clustering with LLM-Generated Constraints

arXiv: https://arxiv.org/abs/2601.11118 · HTML: https://mlsanity.com/paper/2601.11118/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 16 Jan 2026.

## Builds on (most cited first, 7 of 7)

- [Large Language Models Enable Few-Shot Clustering](https://mlsanity.com/paper/2307.00524/) (2023)
- [ClusterLLM: Large Language Models as a Guide for Text Clustering](https://mlsanity.com/paper/2305.14871/) (2023)
- [Text Clustering as Classification with LLMs](https://mlsanity.com/paper/2410.00927/) (2024)
- [Bipartite Matching in Nearly-linear Time on Moderately Dense Graphs](https://mlsanity.com/paper/2009.01802/) (2020)
- [An algorithm for clustering with confidence-based must-link and cannot-link constraints](https://mlsanity.com/paper/2212.14437/) (2022)
- [A Binary Optimization Approach for Constrained K-Means Clustering](https://mlsanity.com/paper/1810.10134/) (2018)
- [A3S: A General Active Clustering Method with Pairwise Constraints](https://mlsanity.com/paper/2407.10196/) (2024)

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

- [Parameterized and Streaming Algorithms for Euclidean Fair k-Center Clustering](https://mlsanity.com/paper/2609.06384/) (2026)
- [RAILS: Retrieval-Augmented Incremental LLM Clustering at Scale](https://mlsanity.com/paper/2609.24464/) (2026)

## Abstract (arXiv)

Clustering is a fundamental tool that has garnered significant interest across a wide range of applications including text analysis. To improve clustering accuracy, many researchers have incorporated background knowledge, typically in the form of must-link and cannot-link constraints, to guide the clustering process. With the recent advent of large language models (LLMs), there is growing interest in improving clustering quality through LLM-based automatic constraint generation. In this paper, we propose a novel constraint-generation approach that reduces resource consumption by generating constraint sets rather than using traditional pairwise constraints. This approach improves both query efficiency and constraint accuracy compared to state-of-the-art methods. We further introduce a constrained clustering algorithm tailored to the characteristics of LLM-generated constraints. Our method incorporates a confidence threshold and a penalty mechanism to address potentially inaccurate constraints. We evaluate our approach on five text datasets, considering both the cost of constraint generation and the overall clustering performance. The results show that our method achieves clustering accuracy comparable to the state-of-the-art algorithms while reducing the number of LLM queries by more than 20 times.