{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Petr Koráb",
  "title_listed": "Topic Model Labelling with LLMs",
  "keywords": "",
  "domain": "大模型算法",
  "content_type": "深度技术解析",
  "is_paid_flag": "否",
  "url": "https://towardsdatascience.com/topic-model-labelling-with-llms/",
  "publish_date": "2025-07-15",
  "corpus_stage": "",
  "notes": "",
  "id": "2d30543eb2ba",
  "collected_at": "2026-10-02T03:36:22",
  "title": "Topic Model Labelling with LLMs | Towards Data Science",
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It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Large Language Models](https://towardsdatascience.com/category/artificial-intelligence/large-language-models/)\n# Topic Model Labelling with LLMs\nPython tutorial for reproducible labeling of cutting-edge topic models with GPT4-o-mini.\n[Petr Koráb](https://towardsdatascience.com/author/petrkorab/)\nJuly 14, 20256 min read\n![](../images/000.png)\nImage by authors\n**By** : _Petr Koráb_ *, _Martin Feldkircher**, *** Viktoriya Teliha_ ** (*Text Mining Stories, Prague, **Vienna School of International Studies, ***Centre for Applied Macroeconomic Analysis, Australia).\n**Manual labeling** of terms produced by topic models requires domain experience and may be subjective to the labeler. Especially when the number of topics grows large, it might be convenient to assign human-readable names to topics automatically with an LLM. Simply copying and pasting the results into UIs, such as [chatgpt.com,](https://chatgpt.com) is quite a “black-box” and unsystematic. A better choice would be to add topic labeling to the code with a documented labeler, which gives the engineer more control over the results and ensures reproducibility. This tutorial will explore in detail:\n  * How to train a topic model with a fresh new **Turftopic** Python package\n  * How to label topic model results with GPT-4.0 mini.\n\n\nWe will train a cutting-edge **FASTopic** model by [Xiaobao Wu et al. [3](https://arxiv.org/pdf/2405.17978)] presented at last year’s [NeurIPS](https://neurips.cc/virtual/2024/poster/96416). This model [outperforms other competing models](https://medium.com/text-mining-stories/choose-the-right-one-evaluating-topic-models-for-business-intelligence-1e2f418d7573?sk=3bc07126cc44a1d254fb8a0967702957), such as **BERTopic** , in several key metrics (e.g., topic diversity) and has broad [applications in business intelligence.](https://medium.com/data-science/topic-modelling-in-business-intelligence-fastopic-and-bertopic-in-code-2d3949260a37?sk=9a88660d4e4c64a1d91ad8ede730a520)\nLearn this step by step with the interactive [AI Engineer](https://roadmap.sh/ai-engineer) roadmap.\n## 1. Components of the Topic Modelling Pipeline\nLabelling is the essential part of the topic modelling pipeline because it bridges the model outputs with real-world decisions. The model assigns a number to each topic, but a business decision relies on the human-readable text label summarizing the typical terms in each topic. The models are typically labelled by (1) labellers with the domain experience, often using a well-defined labelling strategy, (2) LLMs, and (3) commercial tools. The path from raw data to decision-making through a topic model is nicely explained in Image 1.\n![](../images/001.png)\nImage 1. Components of the topic modeling pipeline.  \nSource: adapted and extended from Kardos et al [2].\nThe pipeline starts with raw data, which is preprocessed and vectorized for the topic model. The model returns topics named with integers, including typical terms (words or bigrams). The labeling layer replaces the integer in the topic name with the text label. The model user ([product manager](https://xenoss.io/blog/topic-modeling-for-business-introduction), [customer care ](https://medium.com/text-mining-stories/choose-the-right-one-evaluating-topic-models-for-business-intelligence-1e2f418d7573?sk=3bc07126cc44a1d254fb8a0967702957)dept., etc.) then works with labelled terms to make data-informed decisions. In the following modeling example, we will follow it step by step.\n## 2. Data\nWe will use FASTopic to classify customer complaints data into 10 topics. The example use case uses a synthetically generated [Customer Care Email](https://www.kaggle.com/datasets/rtweera/customer-care-emails) dataset available on Kaggle, licensed under the [GPL-3 license](https://www.gnu.org/licenses/gpl-3.0.html). The prefiltered data covers 692 incoming emails to the customer care department and looks like this:\n![](../images/002.png)\nImage 2. [Customer Care Email ](https://www.kaggle.com/datasets/rtweera/customer-care-emails)dataset. Image by authors.\n### 2.1. Data preprocessing\nText data is sequentially preprocessed in six steps. Numbers are removed first, followed by emojis. English stopwords are removed afterward, followed by punctuation. Additional tokens (such as company and person names) are removed in the next step before lemmatization. Read more on text preprocessing for topic models in our [previous tutorial](https://medium.com/data-science/topic-modelling-in-business-intelligence-fastopic-and-bertopic-in-code-2d3949260a37?sk=9a88660d4e4c64a1d91ad8ede730a520).\nFirst, we read the clean data and tokenize the dataset:\npython\n\n```\nimport pandas as pd\n\n# Read data\ndata = pd.read_csv(\"data.csv\", usecols=['message_clean'])\n\n# Create corpus list\ndocs = data[\"message_clean\"].tolist()\n```\n\n![](../images/003.png)\nImage 3. Recommended cleaning pipeline for topic models. Image by authors.\n### 2.2. Bigram vectorization\nNext, we create a bigram tokenizer to process tokens as bigrams during the model training. Bigram models provide more relevant information and identify better key qualities and problems for business decisions than single-word models _(“delivery” vs. “poor delivery”, “stomach” vs. “sensitive stomach”, etc.)_.\npython\n\n```\nfrom sklearn.feature_extraction.text import CountVectorizer\n\nbigram_vectorizer = CountVectorizer(\n    ngram_range=(2, 2),               # only bigrams\n    max_features=1000                 # top 1000 bigrams by frequency\n)\n```\n\n## 3. Model training\nThe FASTopic model is currently implemented in two Python packages:\n  * [**Fastopic**](https://pypi.org/project/fastopic/): official package by X. Wu\n  * [**Turftopic**](https://pypi.org/project/turftopic/): new Python package that brings many helpful topic modeling features, including labeling with LLMs [2]\n\n\nWe will use the Turftopic implementation because of the direct link between the model and the [Namer](https://x-tabdeveloping.github.io/turftopic/namers/) that offers LLM labelling.\nLet’s set up the model and fit it to the data. It is essential to set a random state to secure training reproducibility.\npython\n\n```\nfrom turftopic import FASTopic\n\n# Model specification\ntopic_size  = 10\nmodel = FASTopic(n_components = topic_size,       # train for 10 topics\n                 vectorizer = bigram_vectorizer,  # generate bigrams in topics\n                 random_state = 32).fit(docs)     # set random state \n\n# Fit model to corpus\ntopic_data = model.prepare_topic_data(docs)\n```\n\nNow, let's prepare a dataframe with topic IDs and the top 10 bigrams with the highest probability received from the model (code is [here](https://github.com/PetrKorab/Topic-Model-Labelling-with-LLMs/blob/main/code.ipynb)).\n![](../images/body-cef5ae7cdde45fb51131.png)\nImage 4. Unlabeled topics in FASTopic. Image by authors.\n## 4. Topic labeling\nIn the next step, we add text labels to the topic IDs with GPT4-o-mini. Let’s follow these steps:\n  * Create an [𝐎𝐩𝐞𝐧 𝐀𝐈 𝐚𝐜𝐜𝐨𝐮𝐧𝐭](https://auth.openai.com/create-account) and choose a billing plan (e.g. “𝘗𝘢𝘺 𝘢𝘴 𝘺𝘰𝘶 𝘨𝘰”)\n  * Set an environment variable with OpenAI API key\n  * Use [Turftopic’s 𝐍𝐚𝐦𝐞𝐫](https://x-tabdeveloping.github.io/turftopic/namers) to label keywords in the topics with the LLM.\n\n\nWith this code, we label the topics and add a new row _topic_name_ to the dataframe.\npython\n\n```\nfrom turftopic.namers import OpenAITopicNamer\nimport os\n\n# OpenAI API key key to access GPT-4\nos.environ[\"OPENAI_API_KEY\"] = \"\"   \n\n# use Namer to label topic model with LLM\nnamer = OpenAITopicNamer(\"gpt-4o-mini\")\nmodel.rename_topics(namer)\n\n# create a dataframe with labelled topics\ntopics_df = model.topics_df()\ntopics_df.columns = ['topic_id', 'topic_name', 'topic_words']\n\n# split and explode\ntopics_df['topic_word'] = topics_df['topic_words'].str.split(',')\ntopics_df = topics_df.explode('topic_word')\ntopics_df['topic_word'] = topics_df['topic_word'].str.strip()\n\n# add a rank for each word within a topic\ntopics_df['word_rank'] = topics_df.groupby('topic_id').cumcount() + 1\n\n# pivot to wide format\nwide = topics_df.pivot(index='word_rank', \n                       columns=['topic_id', 'topic_name'], values='topic_word')\n```\n\nHere is the table with labeled topics after additional transformations. It would be interesting to compare the LLM results with those of a company insider who is familiar with the company's processes and customer base. The dataset is synthetic, so let’s rely on the GPT-4 labeling.\n![](../images/body-e83471467c767d164907.png)\nImage 5. Labeled topics in FASTopic by GPT4–o-mini. Image by authors.\nWe can also visualize the labeled topics for a better presentation. The code for the bigram word cloud visualization, generated from the topics produced by the model, is [here](https://github.com/PetrKorab/Topic-Model-Labelling-with-LLMs).\n![](../images/body-c03f31ae5c2cd75163c0.png)\nImage 6. Word cloud visualization of labeled topics by GPT4–o-mini. Image by authors.\n## Summary\n  * The new Turftopic Python package links recent topic models with the [LLM-based labeler](https://x-tabdeveloping.github.io/turftopic/namers/) for generating human-readable topic names.\n  * The main benefits are: 1) independence from the labeler’s subjective experience, 2) capacity to label models with a large number of topics that a human labeler might have difficulty labeling independently, and 3) more control of the code and reproducibility.\n  * Topic labeling with LLMs has a wide range of applications in diverse areas. Read [our latest paper](https://crawford.anu.edu.au/sites/default/files/2025-06/35_2025_Feldkircher_Korab_Teliha_1.pdf) on the topic modeling of central bank communication, where GPT-4 labeled the FASTopic model.\n  * The labels are slightly different for each training, even with the random state. It is not caused by the Namer, but by the random processes in model training that output bigrams with probabilities in descending order. The differences in probabilities are in tiny decimals, so each training generates a few new terms in the top 10, which then impacts the LLM labeler.\n\n\nThe data and complete code for this tutorial are [here.](https://github.com/PetrKorab/Topic-Model-Labelling-with-LLMs)\n_**Petr Korab**_ _is a Senior Data Analyst and Founder of_[ _Text Mining Stories_](https://textminingstories.com/) _with over eight years of experience in Business Intelligence and NLP._\n_Sign up_ _for_[ _our blog_](https://textminingstories.com/blog) _to get the latest news from the NLP industry!_\n## References\n[1] Feldkircher, M., Korab, P., Teliha, V., (2025).“[What Do Central Bankers Talk About? Evidence From the BIS Archive](https://ideas.repec.org/p/een/camaaa/2025-35.html),” CAMA Working Papers 2025–35, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.\n[2] Kardos, M., Enevoldsen, K. C., Kostkan, J., Kristensen-McLachlan, R. D., Rocca, R. (2025). Turftopic: Topic Modelling with Contextual Representations from Sentence Transformers. _Journal of Open Source Software_ , 10(111), 8183, <https://doi.org/10.21105/joss.08183>.\n[3] Wu, X, Nguyen, T., Ce Zhang, D., Yang Wang, W., Luu, A. T. (2024). FASTopic: [A Fast, Adaptive, Stable, and Transferable Topic Modeling Paradigm](https://arxiv.org/abs/2405.17978). arXiv preprint: 2405.17978.\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Large Language Models](https://towardsdatascience.com/category/artificial-intelligence/large-language-models/)\n# Topic Model Labelling with LLMs\nPython tutorial for reproducible labeling of cutting-edge topic models with GPT4-o-mini.\n[Petr Koráb](https://towardsdatascience.com/author/petrkorab/)\nJuly 14, 20256 min read\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/07/FAST_10_wordcloud.png)\nImage by authors\n**By** : _Petr Koráb_ *, _Martin Feldkircher**, *** Viktoriya Teliha_ ** (*Text Mining Stories, Prague, **Vienna School of International Studies, ***Centre for Applied Macroeconomic Analysis, Australia).\n**Manual labeling** of terms produced by topic models requires domain experience and may be subjective to the labeler. Especially when the number of topics grows large, it might be convenient to assign human-readable names to topics automatically with an LLM. Simply copying and pasting the results into UIs, such as [chatgpt.com,](https://chatgpt.com) is quite a “black-box” and unsystematic. A better choice would be to add topic labeling to the code with a documented labeler, which gives the engineer more control over the results and ensures reproducibility. This tutorial will explore in detail:\n  * How to train a topic model with a fresh new **Turftopic** Python package\n  * How to label topic model results with GPT-4.0 mini.\n\n\nWe will train a cutting-edge **FASTopic** model by [Xiaobao Wu et al. [3](https://arxiv.org/pdf/2405.17978)] presented at last year’s [NeurIPS](https://neurips.cc/virtual/2024/poster/96416). This model [outperforms other competing models](https://medium.com/text-mining-stories/choose-the-right-one-evaluating-topic-models-for-business-intelligence-1e2f418d7573?sk=3bc07126cc44a1d254fb8a0967702957), such as **BERTopic** , in several key metrics (e.g., topic diversity) and has broad [applications in business intelligence.](https://medium.com/data-science/topic-modelling-in-business-intelligence-fastopic-and-bertopic-in-code-2d3949260a37?sk=9a88660d4e4c64a1d91ad8ede730a520)\nLearn this step by step with the interactive [AI Engineer](https://roadmap.sh/ai-engineer) roadmap.\n## 1. Components of the Topic Modelling Pipeline\nLabelling is the essential part of the topic modelling pipeline because it bridges the model outputs with real-world decisions. The model assigns a number to each topic, but a business decision relies on the human-readable text label summarizing the typical terms in each topic. The models are typically labelled by (1) labellers with the domain experience, often using a well-defined labelling strategy, (2) LLMs, and (3) commercial tools. The path from raw data to decision-making through a topic model is nicely explained in Image 1.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/07/Components-of-topic-modelling-pipeline-1-1024x793.png)\nImage 1. Components of the topic modeling pipeline.  \nSource: adapted and extended from Kardos et al [2].\nThe pipeline starts with raw data, which is preprocessed and vectorized for the topic model. The model returns topics named with integers, including typical terms (words or bigrams). The labeling layer replaces the integer in the topic name with the text label. The model user ([product manager](https://xenoss.io/blog/topic-modeling-for-business-introduction), [customer care ](https://medium.com/text-mining-stories/choose-the-right-one-evaluating-topic-models-for-business-intelligence-1e2f418d7573?sk=3bc07126cc44a1d254fb8a0967702957)dept., etc.) then works with labelled terms to make data-informed decisions. In the following modeling example, we will follow it step by step.\n## 2. Data\nWe will use FASTopic to classify customer complaints data into 10 topics. The example use case uses a synthetically generated [Customer Care Email](https://www.kaggle.com/datasets/rtweera/customer-care-emails) dataset available on Kaggle, licensed under the [GPL-3 license](https://www.gnu.org/licenses/gpl-3.0.html). The prefiltered data covers 692 incoming emails to the customer care department and looks like this:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/07/1m45AM5Blp2AY-woHy84mxQ.png)\nImage 2. [Customer Care Email ](https://www.kaggle.com/datasets/rtweera/customer-care-emails)dataset. Image by authors.\n### 2.1. Data preprocessing\nText data is sequentially preprocessed in six steps. Numbers are removed first, followed by emojis. English stopwords are removed afterward, followed by punctuation. Additional tokens (such as company and person names) are removed in the next step before lemmatization. Read more on text preprocessing for topic models in our [previous tutorial](https://medium.com/data-science/topic-modelling-in-business-intelligence-fastopic-and-bertopic-in-code-2d3949260a37?sk=9a88660d4e4c64a1d91ad8ede730a520).\nFirst, we read the clean data and tokenize the dataset:\npython\n\n```\nimport pandas as pd\n\n# Read data\ndata = pd.read_csv(\"data.csv\", usecols=['message_clean'])\n\n# Create corpus list\ndocs = data[\"message_clean\"].tolist()\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/07/1fIP08_8zJJTRcWAn6DRdiQ.png)\nImage 3. Recommended cleaning pipeline for topic models. Image by authors.\n### 2.2. Bigram vectorization\nNext, we create a bigram tokenizer to process tokens as bigrams during the model training. Bigram models provide more relevant information and identify better key qualities and problems for business decisions than single-word models _(“delivery” vs. “poor delivery”, “stomach” vs. “sensitive stomach”, etc.)_.\npython\n\n```\nfrom sklearn.feature_extraction.text import CountVectorizer\n\nbigram_vectorizer = CountVectorizer(\n    ngram_range=(2, 2),               # only bigrams\n    max_features=1000                 # top 1000 bigrams by frequency\n)\n```\n\n## 3. Model training\nThe FASTopic model is currently implemented in two Python packages:\n  * [**Fastopic**](https://pypi.org/project/fastopic/): official package by X. Wu\n  * [**Turftopic**](https://pypi.org/project/turftopic/): new Python package that brings many helpful topic modeling features, including labeling with LLMs [2]\n\n\nWe will use the Turftopic implementation because of the direct link between the model and the [Namer](https://x-tabdeveloping.github.io/turftopic/namers/) that offers LLM labelling.\nLet’s set up the model and fit it to the data. It is essential to set a random state to secure training reproducibility.\npython\n\n```\nfrom turftopic import FASTopic\n\n# Model specification\ntopic_size  = 10\nmodel = FASTopic(n_components = topic_size,       # train for 10 topics\n                 vectorizer = bigram_vectorizer,  # generate bigrams in topics\n                 random_state = 32).fit(docs)     # set random state \n\n# Fit model to corpus\ntopic_data = model.prepare_topic_data(docs)\n```\n\nNow, let's prepare a dataframe with topic IDs and the top 10 bigrams with the highest probability received from the model (code is [here](https://github.com/PetrKorab/Topic-Model-Labelling-with-LLMs/blob/main/code.ipynb)).\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/07/unlabelled-topics-1-1024x223.png)\nImage 4. Unlabeled topics in FASTopic. Image by authors.\n## 4. Topic labeling\nIn the next step, we add text labels to the topic IDs with GPT4-o-mini. Let’s follow these steps:\n  * Create an [𝐎𝐩𝐞𝐧 𝐀𝐈 𝐚𝐜𝐜𝐨𝐮𝐧𝐭](https://auth.openai.com/create-account) and choose a billing plan (e.g. “𝘗𝘢𝘺 𝘢𝘴 𝘺𝘰𝘶 𝘨𝘰”)\n  * Set an environment variable with OpenAI API key\n  * Use [Turftopic’s 𝐍𝐚𝐦𝐞𝐫](https://x-tabdeveloping.github.io/turftopic/namers) to label keywords in the topics with the LLM.\n\n\nWith this code, we label the topics and add a new row _topic_name_ to the dataframe.\npython\n\n```\nfrom turftopic.namers import OpenAITopicNamer\nimport os\n\n# OpenAI API key key to access GPT-4\nos.environ[\"OPENAI_API_KEY\"] = \"\"   \n\n# use Namer to label topic model with LLM\nnamer = OpenAITopicNamer(\"gpt-4o-mini\")\nmodel.rename_topics(namer)\n\n# create a dataframe with labelled topics\ntopics_df = model.topics_df()\ntopics_df.columns = ['topic_id', 'topic_name', 'topic_words']\n\n# split and explode\ntopics_df['topic_word'] = topics_df['topic_words'].str.split(',')\ntopics_df = topics_df.explode('topic_word')\ntopics_df['topic_word'] = topics_df['topic_word'].str.strip()\n\n# add a rank for each word within a topic\ntopics_df['word_rank'] = topics_df.groupby('topic_id').cumcount() + 1\n\n# pivot to wide format\nwide = topics_df.pivot(index='word_rank', \n                       columns=['topic_id', 'topic_name'], values='topic_word')\n```\n\nHere is the table with labeled topics after additional transformations. It would be interesting to compare the LLM results with those of a company insider who is familiar with the company's processes and customer base. The dataset is synthetic, so let’s rely on the GPT-4 labeling.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/07/labelled-topics-2-1024x267.png)\nImage 5. Labeled topics in FASTopic by GPT4–o-mini. Image by authors.\nWe can also visualize the labeled topics for a better presentation. The code for the bigram word cloud visualization, generated from the topics produced by the model, is [here](https://github.com/PetrKorab/Topic-Model-Labelling-with-LLMs).\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/07/FAST_10_wordcloud-4-1024x267.png)\nImage 6. Word cloud visualization of labeled topics by GPT4–o-mini. Image by authors.\n## Summary\n  * The new Turftopic Python package links recent topic models with the [LLM-based labeler](https://x-tabdeveloping.github.io/turftopic/namers/) for generating human-readable topic names.\n  * The main benefits are: 1) independence from the labeler’s subjective experience, 2) capacity to label models with a large number of topics that a human labeler might have difficulty labeling independently, and 3) more control of the code and reproducibility.\n  * Topic labeling with LLMs has a wide range of applications in diverse areas. Read [our latest paper](https://crawford.anu.edu.au/sites/default/files/2025-06/35_2025_Feldkircher_Korab_Teliha_1.pdf) on the topic modeling of central bank communication, where GPT-4 labeled the FASTopic model.\n  * The labels are slightly different for each training, even with the random state. It is not caused by the Namer, but by the random processes in model training that output bigrams with probabilities in descending order. The differences in probabilities are in tiny decimals, so each training generates a few new terms in the top 10, which then impacts the LLM labeler.\n\n\nThe data and complete code for this tutorial are [here.](https://github.com/PetrKorab/Topic-Model-Labelling-with-LLMs)\n_**Petr Korab**_ _is a Senior Data Analyst and Founder of_[ _Text Mining Stories_](https://textminingstories.com/) _with over eight years of experience in Business Intelligence and NLP._\n_Sign up_ _for_[ _our blog_](https://textminingstories.com/blog) _to get the latest news from the NLP industry!_\n## References\n[1] Feldkircher, M., Korab, P., Teliha, V., (2025).“[What Do Central Bankers Talk About? Evidence From the BIS Archive](https://ideas.repec.org/p/een/camaaa/2025-35.html),” CAMA Working Papers 2025–35, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.\n[2] Kardos, M., Enevoldsen, K. C., Kostkan, J., Kristensen-McLachlan, R. D., Rocca, R. (2025). Turftopic: Topic Modelling with Contextual Representations from Sentence Transformers. _Journal of Open Source Software_ , 10(111), 8183, <https://doi.org/10.21105/joss.08183>.\n[3] Wu, X, Nguyen, T., Ce Zhang, D., Yang Wang, W., Luu, A. T. (2024). FASTopic: [A Fast, Adaptive, Stable, and Transferable Topic Modeling Paradigm](https://arxiv.org/abs/2405.17978). arXiv preprint: 2405.17978.\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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