# Human-Centric Topic Modeling with Goal-Prompted Contrastive Learning and Optimal Transport

arXiv: https://arxiv.org/abs/2604.12663 · HTML: https://mlsanity.com/paper/2604.12663/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 49 references that are in the map.
- First posted on arXiv 14 Apr 2026.

## Builds on (most cited first, 23 of 23)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [Computational Optimal Transport](https://mlsanity.com/paper/1803.00567/) (2018)
- [A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications](https://mlsanity.com/paper/2402.07927/) (2024)
- [Debiased Contrastive Learning](https://mlsanity.com/paper/2007.00224/) (2020)
- [Neural Variational Inference for Text Processing](https://mlsanity.com/paper/1511.06038/) (2015)
- [Goal-Driven Explainable Clustering via Language Descriptions](https://mlsanity.com/paper/2305.13749/) (2023)
- [Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling](https://mlsanity.com/paper/2403.16248/) (2024)
- [A note on the triangle inequality for the Jaccard distance](https://mlsanity.com/paper/1612.02696/) (2016)
- [ATM:Adversarial-neural Topic Model](https://mlsanity.com/paper/1811.00265/) (2018)
- [vONTSS: vMF based semi-supervised neural topic modeling with optimal transport](https://mlsanity.com/paper/2307.01226/) (2023)
- [Open Event Extraction from Online Text using a Generative Adversarial Network](https://mlsanity.com/paper/1908.09246/) (2019)
- [Neural Topic Modeling with Large Language Models in the Loop](https://mlsanity.com/paper/2411.08534/) (2024)
- [CWTM: Leveraging Contextualized Word Embeddings from BERT for Neural Topic Modeling](https://mlsanity.com/paper/2305.09329/) (2023)
- [Improving Contextualized Topic Models with Negative Sampling](https://mlsanity.com/paper/2303.14951/) (2023)
- [CAST: Corpus-Aware Self-similarity Enhanced Topic modelling](https://mlsanity.com/paper/2410.15136/) (2024)
- [A Large Language Model Guided Topic Refinement Mechanism for Short Text Modeling](https://mlsanity.com/paper/2403.17706/) (2024)

## Abstract (arXiv)

Existing topic modeling methods, from LDA to recent neural and LLM-based approaches, which focus mainly on statistical coherence, often produce redundant or off-target topics that miss the user's underlying intent. We introduce Human-centric Topic Modeling, Human-TM), a novel task formulation that integrates a human-provided goal directly into the topic modeling process to produce interpretable, diverse and goal-oriented topics. To tackle this challenge, we propose the Goal-prompted Contrastive Topic Model with Optimal Transport (GCTM-OT), which first uses LLM-based prompting to extract goal candidates from documents, then incorporates these into semantic-aware contrastive learning via optimal transport for topic discovery. Experimental results on three public subreddit datasets show that GCTM-OT outperforms state-of-the-art baselines in topic coherence and diversity while significantly improving alignment with human-provided goals, paving the way for more human-centric topic discovery systems.