Summary of Autotm 2.0: Automatic Topic Modeling Framework For Documents Analysis, by Maria Khodorchenko and Nikolay Butakov and Maxim Zuev and Denis Nasonov
AutoTM 2.0: Automatic Topic Modeling Framework for Documents Analysis
by Maria Khodorchenko, Nikolay Butakov, Maxim Zuev, Denis Nasonov
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed AutoTM 2.0 framework optimizes additively regularized topic models by introducing a novel optimization pipeline, leveraging Large Language Model (LLM) based quality metrics and distributed mode capabilities. This improvement is showcased through benchmarking on several datasets. The framework’s performance is evaluated using various metrics, including perplexity and coherence scores. By addressing limitations of the previous AutoTM version, this work enables more efficient and effective topic modeling for various applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to improve topic models by developing an AutoTM 2.0 framework. The main innovation includes a new optimization process, using AI-powered quality metrics, and allowing it to run on multiple computers at once. This helps make the model more efficient and accurate. The results are tested on different datasets and show promising performance. |
Keywords
» Artificial intelligence » Large language model » Optimization » Perplexity
