Uploaded July 2025 | Updated September 2026, 5 hours ago
Maarten Grootendorst is a psychologist turned AI engineer who has created BERTopic and authored "Hands-On Large Language Models" with Jay Alammar. The rise of LLMs and Agents are transforming many areas of software! This podcast dives deep into their impact on Topic Modeling! Maarten designed BERTopic from the start with modularity in mind -- letting you ablate embedding models, dimensionality reduction, clustering algorithms, and more. This early insight to prioritize modularity makes BERTopic incredibly well structured to become more "Agentic". An "Agentic" Topic Modeling algorithm can use LLMs to generate topics or topic descriptions, as well as contrast them with other topics. It can decide which topics to subdivide, and it can integrate human feedback and evaluate topics in novel ways... I hope you find the podcast interesting!
Links:
Hands-On Large Language Models: oreilly.com/library/view/hands-on-large-language/9781098150952
BERTopic: github.com/MaartenGr/BERTopic
BERTopic (paper): arxiv.org/abs/2203.05794
Learn more about Maarten Grootendorst: maartengrootendorst.com
TopicGPT: arxiv.org/abs/2311.01449
TnT-LLM: arxiv.org/abs/2403.12173
Chapters
0:00 Welcome Maarten!
1:57 Hands-On Large Language Models
7:34 An Overview of Topic Modeling
10:45 LLM Topic Generation
17:13 Topic Modeling with Human Feedback
21:33 Topic Granularity
26:00 Visualizing Topics
31:18 Contrastive Topics
33:24 LLM-as-Judge for Topics
39:06 Separating Generation from Assignment
44:20 Applications of Topic Modeling
55:14 Semi-Supervised BERTopic
1:01:06 Future Directions for AI
Maarten Grootendorst is a psychologist turned AI engineer who has created BERTopic and authored "Hands-On Large Language Models" with Jay Alammar. The rise of LLMs and Agents are transforming many areas of software! This podcast dives deep into their impact on Topic Modeling! Maarten designed BERTopic from the start with modularity in mind -- letting you ablate embedding models, dimensionality reduction, clustering algorithms, and more. This early insight to prioritize modularity makes BERTopic incredibly well structured to become more "Agentic". An "Agentic" Topic Modeling algorithm can use LLMs to generate topics or topic descriptions, as well as contrast them with other topics. It can decide which topics to subdivide, and it can integrate human feedback and evaluate topics in novel ways... I hope you find the podcast interesting!
Links:
Hands-On Large Language Models: oreilly.com/library/view/hands-on-large-language/9781098150952
BERTopic: github.com/MaartenGr/BERTopic
BERTopic (paper): arxiv.org/abs/2203.05794
Learn more about Maarten Grootendorst: maartengrootendorst.com
TopicGPT: arxiv.org/abs/2311.01449
TnT-LLM: arxiv.org/abs/2403.12173
Chapters
0:00 Welcome Maarten!
1:57 Hands-On Large Language Models
7:34 An Overview of Topic Modeling
10:45 LLM Topic Generation
17:13 Topic Modeling with Human Feedback
21:33 Topic Granularity
26:00 Visualizing Topics
31:18 Contrastive Topics
33:24 LLM-as-Judge for Topics
39:06 Separating Generation from Assignment
44:20 Applications of Topic Modeling
55:14 Semi-Supervised BERTopic
1:01:06 Future Directions for AI
