MALTopic: Multi-Agent LLM Topic Modeling Framework

• URL: http://arxiv.org/abs/2601.15299v1
• Date: Wed, 07 Jan 2026 06:32:59 GMT
• Title: MALTopic: Multi-Agent LLM Topic Modeling Framework
• Authors: Yash Sharma,
• Abstract summary: We propose the Multi-Agent LLM Topic Modeling Framework (MALTopic) to decompose topic modeling into specialized tasks.<n>By integrating structured data and employing a multi-agent approach, MALTopic generates human-readable topics with enhanced contextual relevance.
• Score: 2.1182259123493243
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Topic modeling is a crucial technique for extracting latent themes from unstructured text data, particularly valuable in analyzing survey responses. However, traditional methods often only consider free-text responses and do not natively incorporate structured or categorical survey responses for topic modeling. And they produce abstract topics, requiring extensive human interpretation. To address these limitations, we propose the Multi-Agent LLM Topic Modeling Framework (MALTopic). This framework decomposes topic modeling into specialized tasks executed by individual LLM agents: an enrichment agent leverages structured data to enhance textual responses, a topic modeling agent extracts latent themes, and a deduplication agent refines the results. Comparative analysis on a survey dataset demonstrates that MALTopic significantly improves topic coherence, diversity, and interpretability compared to LDA and BERTopic. By integrating structured data and employing a multi-agent approach, MALTopic generates human-readable topics with enhanced contextual relevance, offering a more effective solution for analyzing complex survey data.

Related papers

• S-DAG: A Subject-Based Directed Acyclic Graph for Multi-Agent Heterogeneous Reasoning [22.610518752034043]
  Large Language Models (LLMs) have achieved impressive performance in complex reasoning problems.<n>This work proposes a novel framework that performs fine-grained analysis at subject level equipped with a designated multi-agent collaboration strategy.
  arXiv  Detail & Related papers  (2025-11-10T05:40:02Z)
• Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding [61.36285696607487]
  Document understanding is critical for applications from financial analysis to scientific discovery.<n>Current approaches, whether OCR-based pipelines feeding Large Language Models (LLMs) or native Multimodal LLMs (MLLMs) face key limitations.<n>Retrieval-Augmented Generation (RAG) helps ground models in external data, but documents' multimodal nature, combining text, tables, charts, and layout, demands a more advanced paradigm: Multimodal RAG.
  arXiv  Detail & Related papers  (2025-10-17T02:33:16Z)
• MLego: Interactive and Scalable Topic Exploration Through Model Reuse [12.133380833451573]
  We present MLego, an interactive query framework designed to support real-time topic modeling analysis.<n>Instead of retraining models from scratch, MLego efficiently merges materialized topic models to construct approximate results at interactive speeds.<n>We integrate MLego into a visual analytics prototype system, enabling users to explore large-scale textual datasets through interactive queries.
  arXiv  Detail & Related papers  (2025-08-11T06:06:26Z)
• Creating Targeted, Interpretable Topic Models with LLM-Generated Text Augmentation [0.5120567378386615]
  We use topic modeling and clustering to identify latent patterns in unstructured text data in fields such as political science and sociology.<n>We find that topic modeling using GPT-4 augmentations creates highly interpretable categories that can be used to investigate domain-specific research questions with minimal human guidance.
  arXiv  Detail & Related papers  (2025-04-24T11:14:13Z)
• Retrieval Augmented Generation for Topic Modeling in Organizational Research: An Introduction with Empirical Demonstration [0.0]
  This paper introduces Agentic Retrieval-Augmented Generation (Agentic RAG) as a method for topic modeling with LLMs.<n>It integrates three key components: (1) retrieval, enabling automatized access to external data beyond an LLM's pre-trained knowledge; (2) generation, leveraging LLM capabilities for text synthesis; and (3) agent-driven learning, iteratively refining retrieval and query formulation processes.<n>Our findings demonstrate that the approach is more efficient, interpretable and at the same time achieves higher reliability and validity in comparison to the standard machine learning approach.
  arXiv  Detail & Related papers  (2025-02-28T11:25:11Z)
• A Survey of Model Architectures in Information Retrieval [59.61734783818073]
  The period from 2019 to the present has represented one of the biggest paradigm shifts in information retrieval (IR) and natural language processing (NLP)<n>We trace the development from traditional term-based methods to modern neural approaches, particularly highlighting the impact of transformer-based models and subsequent large language models (LLMs)<n>We conclude with a forward-looking discussion of emerging challenges and future directions.
  arXiv  Detail & Related papers  (2025-02-20T18:42:58Z)
• Personalized Multimodal Large Language Models: A Survey [127.9521218125761]
  Multimodal Large Language Models (MLLMs) have become increasingly important due to their state-of-the-art performance and ability to integrate multiple data modalities.<n>This paper presents a comprehensive survey on personalized multimodal large language models, focusing on their architecture, training methods, and applications.
  arXiv  Detail & Related papers  (2024-12-03T03:59:03Z)
• Retrieval-Enhanced Machine Learning: Synthesis and Opportunities [60.34182805429511]
  Retrieval-enhancement can be extended to a broader spectrum of machine learning (ML) This work introduces a formal framework of this paradigm, Retrieval-Enhanced Machine Learning (REML), by synthesizing the literature in various domains in ML with consistent notations which is missing from the current literature. The goal of this work is to equip researchers across various disciplines with a comprehensive, formally structured framework of retrieval-enhanced models, thereby fostering interdisciplinary future research.
  arXiv  Detail & Related papers  (2024-07-17T20:01:21Z)
• Interactive Topic Models with Optimal Transport [75.26555710661908]
  We present EdTM, as an approach for label name supervised topic modeling. EdTM models topic modeling as an assignment problem while leveraging LM/LLM based document-topic affinities.
  arXiv  Detail & Related papers  (2024-06-28T13:57:27Z)
• Multimodal Relation Extraction with Cross-Modal Retrieval and Synthesis [89.04041100520881]
  This research proposes to retrieve textual and visual evidence based on the object, sentence, and whole image. We develop a novel approach to synthesize the object-level, image-level, and sentence-level information for better reasoning between the same and different modalities.
  arXiv  Detail & Related papers  (2023-05-25T15:26:13Z)
• Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations [35.74225306947918]
  We propose a joint latent space learning and clustering framework built upon PLM embeddings. Our model effectively leverages the strong representation power and superb linguistic features brought by PLMs for topic discovery.
  arXiv  Detail & Related papers  (2022-02-09T17:26:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.