Dynamic Topic Evolution with Temporal Decay and Attention in Large Language Models

• URL: http://arxiv.org/abs/2510.10613v2
• Date: Mon, 03 Nov 2025 02:01:32 GMT
• Title: Dynamic Topic Evolution with Temporal Decay and Attention in Large Language Models
• Authors: Di Wu, Shuaidong Pan,
• Abstract summary: This paper proposes a modeling framework for dynamic topic evolution based on temporal large language models.<n>The proposed method provides a systematic solution for understanding dynamic semantic patterns in large-scale text.
• Score: 3.4219049032524804
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes a modeling framework for dynamic topic evolution based on temporal large language models. The method first uses a large language model to obtain contextual embeddings of text and then introduces a temporal decay function and an attention mechanism. These components allow the model to adjust the importance of semantic units according to time intervals and capture topic variations across different periods. The temporal representations are then mapped into a latent topic space, where a state transition matrix is applied to describe the dynamic evolution of topics. A joint optimization objective constrains both semantic modeling and temporal consistency, ensuring diversity and smoothness in topic generation. The design emphasizes the unified modeling of semantic representation and temporal evolution, which improves topic coherence and diversity while enhancing stability and interpretability over time. Experiments on real-world corpora show that the framework effectively captures the generation, expansion, and decline of topics and outperforms existing models across multiple metrics. Overall, the proposed method provides a systematic solution for understanding dynamic semantic patterns in large-scale text, enriches the research paradigm of topic modeling, and supports complex text analysis tasks in multiple domains.

Related papers

• A Multiscale Geometric Method for Capturing Relational Topic Alignment [0.0]
  Interpretable topic modeling is essential for tracking how research interests evolve within co-author communities.<n>We propose a method that integrates multimodal text and co-author network data, using Hellinger distances and Ward's linkage to construct a hierarchical topic dendrogram.<n>Our method effectively identifies rare-topic structure and visualizes smooth topic drift over time.
  arXiv  Detail & Related papers  (2025-11-21T22:45:16Z)
• UniAlignment: Semantic Alignment for Unified Image Generation, Understanding, Manipulation and Perception [54.53657134205492]
  UniAlignment is a unified multimodal generation framework within a single diffusion transformer.<n>It incorporates both intrinsic-modal semantic alignment and cross-modal semantic alignment, thereby enhancing the model's cross-modal consistency and instruction-following robustness.<n>We present SemGen-Bench, a new benchmark specifically designed to evaluate multimodal semantic consistency under complex textual instructions.
  arXiv  Detail & Related papers  (2025-09-28T09:11:30Z)
• Learning Time-Aware Causal Representation for Model Generalization in Evolving Domains [50.66049136093248]
  We develop a time-aware structural causal model (SCM) that incorporates dynamic causal factors and the causal mechanism drifts.<n>We show that our method can yield the optimal causal predictor for each time domain.<n>Results on both synthetic and real-world datasets exhibit that SYNC can achieve superior temporal generalization performance.
  arXiv  Detail & Related papers  (2025-06-21T14:05:37Z)
• Visualizing Temporal Topic Embeddings with a Compass [1.5184974790808403]
  This paper proposes an expansion of the compass-aligned temporal Word2Vec methodology into dynamic topic modeling. Such a method allows for the direct comparison of word and document embeddings across time in dynamic topics.
  arXiv  Detail & Related papers  (2024-09-16T18:29:19Z)
• A Large Language Model Guided Topic Refinement Mechanism for Short Text Modeling [10.589126787499973]
  Existing topic models often struggle to accurately capture the underlying semantic patterns of short texts.<n>This paper introduces a novel model-agnostic mechanism, termed Topic Refinement.<n>We show that Topic Refinement boosts the topic quality and improves the performance in topic-related text classification tasks.
  arXiv  Detail & Related papers  (2024-03-26T13:50:34Z)
• ANTM: An Aligned Neural Topic Model for Exploring Evolving Topics [1.854328133293073]
  This paper presents an algorithmic family of dynamic topic models called Aligned Neural Topic Models (ANTM) ANTM combines novel data mining algorithms to provide a modular framework for discovering evolving topics. A Python package is developed for researchers and scientists who wish to study the trends and evolving patterns of topics in large-scale textual data.
  arXiv  Detail & Related papers  (2023-02-03T02:31:12Z)
• Learning to Model Multimodal Semantic Alignment for Story Visualization [58.16484259508973]
  Story visualization aims to generate a sequence of images to narrate each sentence in a multi-sentence story. Current works face the problem of semantic misalignment because of their fixed architecture and diversity of input modalities. We explore the semantic alignment between text and image representations by learning to match their semantic levels in the GAN-based generative model.
  arXiv  Detail & Related papers  (2022-11-14T11:41:44Z)
• Learning Semantic Textual Similarity via Topic-informed Discrete Latent Variables [17.57873577962635]
  We develop a topic-informed discrete latent variable model for semantic textual similarity. Our model learns a shared latent space for sentence-pair representation via vector quantization. We show that our model is able to surpass several strong neural baselines in semantic textual similarity tasks.
  arXiv  Detail & Related papers  (2022-11-07T15:09:58Z)
• Model Criticism for Long-Form Text Generation [113.13900836015122]
  We apply a statistical tool, model criticism in latent space, to evaluate the high-level structure of generated text. We perform experiments on three representative aspects of high-level discourse -- coherence, coreference, and topicality. We find that transformer-based language models are able to capture topical structures but have a harder time maintaining structural coherence or modeling coreference.
  arXiv  Detail & Related papers  (2022-10-16T04:35:58Z)
• Knowledge-Aware Bayesian Deep Topic Model [50.58975785318575]
  We propose a Bayesian generative model for incorporating prior domain knowledge into hierarchical topic modeling. Our proposed model efficiently integrates the prior knowledge and improves both hierarchical topic discovery and document representation.
  arXiv  Detail & Related papers  (2022-09-20T09:16:05Z)
• How Far are We from Effective Context Modeling? An Exploratory Study on Semantic Parsing in Context [59.13515950353125]
  We present a grammar-based decoding semantic parsing and adapt typical context modeling methods on top of it. We evaluate 13 context modeling methods on two large cross-domain datasets, and our best model achieves state-of-the-art performances.
  arXiv  Detail & Related papers  (2020-02-03T11:28:10Z)

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.