Papers Topic Models
“Topic Models” 태그가 달린 논문 928편 · 필터 해제
Disentangling Similarity and Relatedness in Topic Models
The recent success of large pre-trained language models (PLMs) has motivated their integration into topic modeling. However, PLM-augmented topic models differ from classical co-occurrence models such as Latent Dirichlet …
Topic ModelsA Neural Topic Method Using a Large-Language-Model-in-the-Loop for Business Research
The growing use of unstructured text in business research makes topic modeling a central tool for constructing explanatory variables from reviews, social media, and open-ended survey responses, yet existing approaches fu…
Topic ModelsWhen Numbers Tell Half the Story: Human-Metric Alignment in Topic Model Evaluation
Topic models uncover latent thematic structures in text corpora, yet evaluating their quality remains challenging, particularly in specialized domains. Existing methods often rely on automated metrics like topic coherenc…
Topic ModelsImproving Topic Modeling by Distilling Soft Labels from Language Models
Traditional neural topic models are typically optimized by reconstructing the document's Bag-of-Words (BoW) representations, overlooking contextual information and struggling with data sparsity. In this work, we introduc…
Topic ModelsTopeax -- An Improved Clustering Topic Model with Density Peak Detection and Lexical-Semantic Term Importance
Text clustering is today the most popular paradigm for topic modelling, both in academia and industry. Despite clustering topic models' apparent success, we identify a number of issues in Top2Vec and BERTopic, which rema…
Text ClusteringTopic ModelsTopic Modelling Black Box Optimization
Choosing the number of topics $T$ in Latent Dirichlet Allocation (LDA) is a key design decision that strongly affects both the statistical fit and interpretability of topic models. In this work, we formulate the selectio…
Topic ModelsSparse Autoencoders are Topic Models
Sparse autoencoders (SAEs) are used to analyze embeddings, but their role and practical value are debated. We propose a new perspective on SAEs by demonstrating that they can be naturally understood as topic models. We p…
Topic ModelsRepeatability is not recovery: Quantifying algorithmic stability and topic recovery in Latent Dirichlet Allocation
Topic models are often judged by the consistency of their outputs across repeated runs, implicitly assuming that repeatable topic output is a successful recovery of the underlying topics. We show that this assumption is …
Information RetrievalTopic ModelsTopic Analysis with Side Information: A Neural-Augmented LDA Approach
Traditional topic models such as Latent Dirichlet Allocation (LDA) have been widely used to uncover latent structures in text corpora, but they often struggle to integrate auxiliary information such as metadata, user att…
Representation LearningTopic ModelsStick-Breaking Embedded Topic Model with Continuous Optimal Transport for Online Analysis of Document Streams
Online topic models are unsupervised algorithms to identify latent topics in data streams that continuously evolve over time. Although these methods naturally align with real-world scenarios, they have received considera…
Topic ModelsDecoding Funded Research: Comparative Analysis of Topic Models and Uncovering the Effect of Gender and Geographic Location
Optimizing national scientific investment requires a clear understanding of evolving research trends and the demographic and geographical forces shaping them, particularly in light of commitments to equity, diversity, an…
Topic ModelsMapping Scientific Literature with Large Language Models and Topic Modeling
Scientific literature is increasingly fragmented by disciplinary boundaries, specialized terminology, and potentially sparse keyword systems, making it difficult to capture the evolving structure of modern science. This …
Topic ModelsLLM as Attention-Informed NTM and Topic Modeling as long-input Generation: Interpretability and long-Context Capability
Topic modeling aims to produce interpretable topic representations and topic--document correspondences from corpora, but classical neural topic models (NTMs) remain constrained by limited representation assumptions and s…
Keyword ExtractionTopic ModelsLogiPart: Local Large Language Models for Data Exploration at Scale with Logical Partitioning
The discovery of deep, steerable taxonomies in large text corpora is currently restricted by a trade-off between the surface-level efficiency of topic models and the prohibitive, non-scalable assignment costs of LLM-inte…
Natural Language InferenceTopic ModelsGRAB: A Risk Taxonomy--Grounded Benchmark for Unsupervised Topic Discovery in Financial Disclosures
Risk categorization in 10-K risk disclosures matters for oversight and investment, yet no public benchmark evaluates unsupervised topic models for this task. We present GRAB, a finance-specific benchmark with 1.61M sente…
Topic ModelsToward Purpose-oriented Topic Model Evaluation enabled by Large Language Models
This study presents a framework for automated evaluation of dynamically evolving topic models using Large Language Models (LLMs). Topic modeling is essential for organizing and retrieving scholarly content in digital lib…
Topic ModelsIDEAlign: Comparing Ideas of Large Language Models to Domain Expert
Large language models (LLMs) are increasingly used to produce open-ended, interpretive annotations, yet there is no validated, scalable measure of idea-level similarity to expert annotations. We (i) introduce the content…
Topic ModelsTMT: A Simple Way to Translate Topic Models Using Dictionaries
The training of topic models for a multilingual environment is a challenging task, requiring the use of sophisticated algorithms, topic-aligned corpora, and manual evaluation. These difficulties are further exacerbated w…
Topic ModelsContinual Neural Topic Model
In continual learning, our aim is to learn a new task without forgetting what was learned previously. In topic models, this translates to learning new topic models without forgetting previously learned topics. Previous w…
Continual LearningTopic ModelsModel Directions, Not Words: Mechanistic Topic Models Using Sparse Autoencoders
Traditional topic models are effective at uncovering latent themes in large text collections. However, due to their reliance on bag-of-words representations, they struggle to capture semantically abstract features. While…
Text GenerationTopic Models