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A Large Language Model Guided Topic Refinement Mechanism for Short Text Modeling

Shuyu Chang, Rui Wang, Peng Ren, Qi Wang, Haiping Huang

TL;DR

This work tackles the challenge of modeling topics in short texts by addressing data sparsity that degrades coherence and granularity. It introduces Topic Refinement, a model-agnostic post-processing mechanism that uses prompt-engineered LLMs to identify intruder words in base topics and generate coherent replacement candidates, iterating to obtain refined topics while preserving a bag-of-words representation. Evaluations across four datasets and eight base models show consistent improvements in topic coherence and granularity, as well as downstream text classification performance, with token-cost considerations favoring efficiency. The results demonstrate robust, cross-model gains and highlight GPT-4 as especially effective for refinement, suggesting a practical path to improved short-text topic modeling without reworking core base models. The approach also points to future work on dynamic, LLM-informed adjustments to topic mining pipelines.

Abstract

Modeling topics effectively in short texts, such as tweets and news snippets, is crucial to capturing rapidly evolving social trends. Existing topic models often struggle to accurately capture the underlying semantic patterns of short texts, primarily due to the sparse nature of such data. This nature of texts leads to an unavoidable lack of co-occurrence information, which hinders the coherence and granularity of mined topics. This paper introduces a novel model-agnostic mechanism, termed Topic Refinement, which leverages the advanced text comprehension capabilities of Large Language Models (LLMs) for short-text topic modeling. Unlike traditional methods, this post-processing mechanism enhances the quality of topics extracted by various topic modeling methods through prompt engineering. We guide LLMs in identifying semantically intruder words within the extracted topics and suggesting coherent alternatives to replace these words. This process mimics human-like identification, evaluation, and refinement of the extracted topics. Extensive experiments on four diverse datasets demonstrate that Topic Refinement boosts the topic quality and improves the performance in topic-related text classification tasks.

A Large Language Model Guided Topic Refinement Mechanism for Short Text Modeling

TL;DR

This work tackles the challenge of modeling topics in short texts by addressing data sparsity that degrades coherence and granularity. It introduces Topic Refinement, a model-agnostic post-processing mechanism that uses prompt-engineered LLMs to identify intruder words in base topics and generate coherent replacement candidates, iterating to obtain refined topics while preserving a bag-of-words representation. Evaluations across four datasets and eight base models show consistent improvements in topic coherence and granularity, as well as downstream text classification performance, with token-cost considerations favoring efficiency. The results demonstrate robust, cross-model gains and highlight GPT-4 as especially effective for refinement, suggesting a practical path to improved short-text topic modeling without reworking core base models. The approach also points to future work on dynamic, LLM-informed adjustments to topic mining pipelines.

Abstract

Modeling topics effectively in short texts, such as tweets and news snippets, is crucial to capturing rapidly evolving social trends. Existing topic models often struggle to accurately capture the underlying semantic patterns of short texts, primarily due to the sparse nature of such data. This nature of texts leads to an unavoidable lack of co-occurrence information, which hinders the coherence and granularity of mined topics. This paper introduces a novel model-agnostic mechanism, termed Topic Refinement, which leverages the advanced text comprehension capabilities of Large Language Models (LLMs) for short-text topic modeling. Unlike traditional methods, this post-processing mechanism enhances the quality of topics extracted by various topic modeling methods through prompt engineering. We guide LLMs in identifying semantically intruder words within the extracted topics and suggesting coherent alternatives to replace these words. This process mimics human-like identification, evaluation, and refinement of the extracted topics. Extensive experiments on four diverse datasets demonstrate that Topic Refinement boosts the topic quality and improves the performance in topic-related text classification tasks.
Paper Structure (25 sections, 4 equations, 6 figures, 6 tables, 1 algorithm)

This paper contains 25 sections, 4 equations, 6 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related work
  3. Short Text Topic Modeling
  4. Large Language Models
  5. Methodology
  6. Problem Definition
  7. Topic Refinement
  8. Prompt Construction
  9. LLM Request and Response
  10. Iterative Refinement
  11. Experiments
  12. Experimental Setup
  13. Datasets.
  14. Base Models.
  15. Evaluation Metrics.
  16. ...and 10 more sections

Figures (6)

  • Figure 1: Workflow of base topic models and our Topic Refinement mechanism.
  • Figure 2: Overview of Topic Refinement ($N=10$): ① Prompt Construction; ② LLM Request and Response; ③ Iterative Refinement.
  • Figure 3: LLM prompt template for Topic Refinement.
  • Figure 4: Ablation study results with various LLMs. The error bars denote a 95% confidence interval for the statistical variability of results across nine base models.
  • Figure 5: Number of word changes between base topics and refined topics.
  • ...and 1 more figures