# TextClusterLab: An Integrated Framework for Reliable Text Clustering Studies

arXiv: https://arxiv.org/abs/2606.28328 · HTML: https://mlsanity.com/paper/2606.28328/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 17 May 2026.

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Efficient Intent Detection with Dual Sentence Encoders](https://mlsanity.com/paper/2003.04807/) (2020)
- [Multilingual E5 Text Embeddings: A Technical Report](https://mlsanity.com/paper/2402.05672/) (2024)
- [Document Expansion by Query Prediction](https://mlsanity.com/paper/1904.08375/) (2019)
- [MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark](https://mlsanity.com/paper/2008.09335/) (2020)
- [EmbeddingGemma: Powerful and Lightweight Text Representations](https://mlsanity.com/paper/2509.20354/) (2025)
- [Sentence Embedding Leaks More Information than You Expect: Generative Embedding Inversion Attack to Recover the Whole Sentence](https://mlsanity.com/paper/2305.03010/) (2023)
- [ClusterLLM: Large Language Models as a Guide for Text Clustering](https://mlsanity.com/paper/2305.14871/) (2023)
- [A Comparative Analysis of Community Detection Algorithms on Artificial Networks](https://mlsanity.com/paper/1608.00763/) (2016)
- [To Cluster, or Not to Cluster: An Analysis of Clusterability Methods](https://mlsanity.com/paper/1808.08317/) (2018)
- [When does Privileged Information Explain Away Label Noise?](https://mlsanity.com/paper/2303.01806/) (2023)
- [Dynamic Label Name Refinement for Few-Shot Dialogue Intent Classification](https://mlsanity.com/paper/2412.15603/) (2024)
- [Natural Language-Based Synthetic Data Generation for Cluster Analysis](https://mlsanity.com/paper/2303.14301/) (2023)
- [Information-Theoretic Generative Clustering of Documents](https://mlsanity.com/paper/2412.13534/) (2024)

## Abstract (arXiv)

In recent years, text clustering has become a critical technique for applications including intent discovery, topic mining, and recommendation systems. However, evaluating text clustering algorithms remains challenging since many real-world textual datasets are not suitable for clustering assessment due to ambiguous semantic boundaries, the high dimensionality of embeddings, and inconsistent cluster structure. Current clustering dataset generators are designed for numerical data, providing limited support for text-specific benchmarking. This paper introduces TextClusterLab, a comprehensive framework for text clustering research. TextClusterLab offers a Large Language Model (LLM)-driven text clustering dataset generator to produce synthetic text datasets for evaluating clustering algorithms. This generator supports setting various clustering attributes, such as class imbalance, intra-cluster compactness, and inter-cluster diversity. These generated datasets can serve as practical benchmarks for testing the robustness and versatility of text clustering algorithms in diverse scenarios. Moreover, we introduce a benchmark to verify whether a text dataset is suitable for clustering evaluation. Therefore, TextClusterLab provides an integrated framework for reproducible and comprehensive text-specific clustering research. Our TextClusterLab is publicly available at https://github.com/research-paper-code/TextClusterLab, and some synthetic example datasets with various attributes are publicly available at https://huggingface.co/datasets/DW-irlab/TextClusterLab.