Summary of Topic Modeling with Fine-tuning Llms and Bag Of Sentences, by Johannes Schneider
Topic Modeling with Fine-tuning LLMs and Bag of Sentences
by Johannes Schneider
First submitted to arxiv on: 6 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 This paper proposes a novel approach to topic modeling, leveraging large language models (LLMs) and fine-tuning techniques. The authors develop an unsupervised method called FT-Topic, which constructs a training dataset by identifying pairs of sentence groups with similar or distinct topics. This approach outperforms classical topic models like Latent Dirichlet Allocation (LDA). Furthermore, the paper presents SenClu, a state-of-the-art topic modeling method that utilizes expectation-maximization algorithms and hard assignments for fast inference. The authors demonstrate the effectiveness of their approach by providing code for reproducibility. The proposed methods can be used with any topic modeling approach using embeddings, making them suitable for various applications such as text classification, clustering, and information retrieval. By leveraging pre-trained LLM encoders like BERT, the authors show that fine-tuning can significantly improve topic modeling performance. Overall, this paper contributes to the development of efficient and effective topic modeling techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about improving a way to understand topics in large amounts of text data. The authors use special language models and training methods to create better topic models than previous ones. They propose a new approach called SenClu, which can quickly find topics in big datasets. This method allows users to add their own knowledge about the topics, making it more accurate. The paper shows that fine-tuning these language models makes them work even better for topic modeling tasks. The authors also provide code so others can reproduce and build upon their research. Overall, this study helps improve our ability to understand and analyze large amounts of text data. |
Keywords
» Artificial intelligence » Bert » Clustering » Fine tuning » Inference » Text classification » Unsupervised
