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

2026-04-14

AI Summary

This work proposes a human-centered topic modeling approach that explicitly incorporates user intent into the modeling process, addressing the tendency of traditional topic models to generate redundant or misaligned topics due to their lack of explicit goal modeling. The method leverages large language model prompting to extract goal-relevant candidate words and integrates optimal transport with semantic-aware contrastive learning for topic discovery. Evaluated on three Reddit datasets, the approach significantly outperforms existing methods, achieving marked improvements in topic coherence, diversity, and alignment with human goals. The resulting topics are not only more interpretable and varied but also directly oriented toward user-specified objectives, demonstrating a principled advance toward purposeful and meaningful topic generation.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Intelligent User Interfaces

Application Category

• Web Mining and Content Analysis: Topic discovery and tracking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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, \emph{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 \textbf{G}oal-prompted \textbf{C}ontrastive \textbf{T}opic \textbf{M}odel with \textbf{O}ptimal \textbf{T}ransport (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.

Problem

• topic modeling
• human intent
• goal-oriented topics
• topic redundancy
• topic relevance

Innovation

• Human-centric Topic Modeling
• Goal-prompted Contrastive Learning
• Optimal Transport
• LLM-based Prompting
• Topic Coherence and Diversity