Human-Centric Topic Modeling with Goal-Prompted Contrastive Learning and Optimal Transport
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866918446025932800 |
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| author | Wang, Rui Zheng, Yi Wang, Dongxin Huang, Haiping Yao, Yuanzhi Zhou, Yuxiang Yu, Jialin Torr, Philip |
| author_facet | Wang, Rui Zheng, Yi Wang, Dongxin Huang, Haiping Yao, Yuanzhi Zhou, Yuxiang Yu, Jialin Torr, Philip |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_12663 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Human-Centric Topic Modeling with Goal-Prompted Contrastive Learning and Optimal Transport Wang, Rui Zheng, Yi Wang, Dongxin Huang, Haiping Yao, Yuanzhi Zhou, Yuxiang Yu, Jialin Torr, Philip Artificial Intelligence 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. |
| title | Human-Centric Topic Modeling with Goal-Prompted Contrastive Learning and Optimal Transport |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2604.12663 |