Topic Modeling with Fine-tuning LLMs and Bag of Sentences
arXiv:2408.03099v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used for topic modeling, outperforming classical topic models such as LDA. Commonly, pre-trained LLM encoders such as BERT are used out-of-the-box despite the fact that fine-tuning is known to improve LLMs considerably. The challenge lies in obtaining a suitable labeled dataset for fine-tuning. In this paper, we build on the recent idea of using bags of sentences as the elementary unit for computing topics. Based on this idea, we derive an approach called FT-Topic to perform unsupervised fine-tuning, relying primarily on two steps for constructing a training dataset in an automatic fashion. First, a heuristic method identifies pairs of sentence groups that are assumed to belong either to the same topic or to different topics. Second, we remove sentence pairs that are likely labeled incorrectly. The resulting dataset is then used to fine-tune an encoder LLM, which can be leveraged by any t
arXiv:2408.03099v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used for topic modeling, outperforming classical topic models such as LDA. Commonly, pre-trained LLM encoders such as BERT are used out-of-the-box despite the fact that fine-tuning is known to improve LLMs considerably. The challenge lies in obtaining a suitable labeled dataset for fine-tuning. In this paper, we build on the recent idea of using bags of sentences as the elementary unit for computing topics. Based on this idea, we derive an approach called FT-Topic to perform unsupervised fine-tuning, relying primarily on two steps for constructing a training dataset in an automatic fashion. First, a heuristic method identifies pairs of sentence groups that are assumed to belong either to the same topic or to different topics. Second, we remove sentence pairs that are likely labeled incorrectly. The resulting dataset is then used to fine-tune an encoder LLM, which can be leveraged by any topic modeling approach that uses embeddings. In this work, we demonstrate its effectiveness by deriving a novel state-of-the-art topic modeling method called SenClu. The method achieves fast inference through an expectation-maximization algorithm and hard assignments of sentence groups to a single topic, while allowing users to encode prior knowledge about the topic-document distribution. Code is available at https://github.com/JohnTailor/FT-Topic
Executive Summary
This article proposes a novel topic modeling approach called FT-Topic, which utilizes fine-tuning large language models (LLMs) and bags of sentences to improve upon classical topic models. The authors introduce a heuristic method to identify sentence pairs for fine-tuning and derive an expectation-maximization algorithm for fast inference. The method, SenClu, achieves state-of-the-art results and allows users to encode prior knowledge about the topic-document distribution. While the approach demonstrates promise, it relies on automatic construction of a training dataset, which may raise concerns about data quality and generalizability. The article contributes to the ongoing discussion on the applications of LLMs in topic modeling and highlights the potential benefits of fine-tuning in improving model performance.
Key Points
- ▸ FT-Topic uses fine-tuning LLMs and bags of sentences for topic modeling
- ▸ Heuristic method identifies sentence pairs for fine-tuning
- ▸ Expectation-maximization algorithm achieves fast inference
- ▸ SenClu achieves state-of-the-art results
- ▸ Users can encode prior knowledge about topic-document distribution
Merits
Strength in Fine-tuning
Fine-tuning LLMs can significantly improve model performance, and the authors demonstrate its effectiveness in topic modeling.
Improved Inference
The expectation-maximization algorithm enables fast inference, making the method more practical for large datasets.
Flexibility in Prior Knowledge
The method allows users to incorporate prior knowledge about the topic-document distribution, which can be beneficial in certain applications.
Demerits
Limitation in Data Quality
The reliance on automatic construction of a training dataset may raise concerns about data quality and generalizability.
Potential Overfitting
Fine-tuning may lead to overfitting, especially when the training dataset is small or biased.
Expert Commentary
The article presents a thought-provoking contribution to the topic modeling literature, leveraging the power of LLMs and fine-tuning to improve model performance. While the method demonstrates promise, it is essential to consider the limitations and potential biases in the training dataset. Future work should focus on addressing these concerns and exploring the applications of SenClu in various NLP tasks. Additionally, the article's findings may have implications for policy decisions in areas such as AI research and NLP development.
Recommendations
- ✓ Further research is needed to address the limitations and potential biases in the training dataset.
- ✓ The method should be evaluated on a broader range of NLP tasks to assess its applicability and robustness.
