A Simple and Effective Way to Perform Text Classification and Clustering with Prompt Engineering

Text classification and clustering are two common tasks in natural language processing (NLP) that involve assigning labels or groups to text documents based on their content. For example, text classification can be used to categorize news articles by topic, sentiment analysis, spam detection, etc. Text clustering can be used to discover hidden patterns or themes in a large collection of documents, such as customer reviews, social media posts, etc.

Prompt Engineering for Text Classification and Clustering

However, these tasks are not easy to perform, especially when the data is noisy, diverse, or domain-specific. Traditional machine learning methods require a lot of manual feature engineering and domain knowledge to achieve good results. Deep learning methods can automatically learn features from raw text, but they often require a large amount of labeled data and computational resources to train.

Prompt engineering is a new paradigm that aims to simplify and improve text classification and clustering by leveraging the power of pre-trained language models (PLMs) such as BERT, GPT-3, etc. PLMs are neural networks that have been trained on massive amounts of text data to learn general linguistic knowledge and representations. Prompt engineering involves designing natural language queries or instructions that can elicit the desired output from a PLM without any fine-tuning or additional training.

For example, to classify a text document into one of four categories: sports, politics, entertainment, or business, one can use the following prompt:

Given the following text document, choose the most appropriate category from the list: sports, politics, entertainment, business.

Text: Netflix has announced that it will produce a live-action series based on the popular video game franchise Assassin’s Creed. The series will be developed by Ubisoft’s film and television division and will explore the rich historical settings and characters of the game.

Category:

Some possible outputs from a PLM are:

Category: entertainment

Category: business

Category: entertainment

Category: entertainment

As we can see, the prompt is able to guide the PLM to produce the correct answer (entertainment) most of the time. The prompt can be modified or refined to improve its accuracy or specificity. For example, one can add more examples or constraints to the prompt, such as:

Given the following text document, choose the most appropriate category from the list: sports, politics, entertainment, business. If none of the categories apply, write “other”.

Text: Netflix has announced that it will produce a live-action series based on the popular video game franchise Assassin’s Creed. The series will be developed by Ubisoft’s film and television division and will explore the rich historical settings and characters of the game.

Category:

Some possible outputs from a PLM are:

Category: entertainment

Category: entertainment

Category: entertainment

Category: other

Similarly, to cluster a set of text documents into groups based on their similarity, one can use the following prompt:

Given the following text documents, assign each document a number from 1 to N, where N is the number of clusters you want to create. Documents that belong to the same cluster should have the same number. Documents that are more similar to each other should have lower numbers.

Text 1: Apple has unveiled its new MacBook Pro models with improved performance and battery life. The new laptops feature a redesigned keyboard, a high-resolution display, and a touch bar that adapts to different applications.

Text 2: Samsung has launched its new Galaxy S21 smartphones with advanced cameras and processors. The new phones come in three sizes and colors, and support 5G connectivity and wireless charging.

Text 3: Tesla has announced that it will start accepting bitcoin as a form of payment for its electric vehicles. The company said that it has invested $1.5 billion in the cryptocurrency and expects to increase its exposure in the future.

Text 4: Pfizer and BioNTech have reported that their COVID-19 vaccine is more than 90% effective in preventing infection. The vaccine is based on a novel technology that uses messenger RNA to instruct cells to produce antibodies.

Some possible outputs from a PLM are:

Text 1: 1

Text 2: 1

Text 3: 2

Text 4: 3

Text 1: 1

Text 2: 2

Text 3: 3

Text 4: 4

Text 1: 1

Text 2: 2

Text 3: 2

Text 4: 3

As we can see, the prompt is able to guide the PLM to produce reasonable clusters based on the content of the documents. The prompt can be modified or refined to improve its quality or granularity. For example, one can add more examples or constraints to the prompt, such as:

Given the following text documents, assign each document a number from 1 to N, where N is the number of clusters you want to create. Documents that belong to the same cluster should have the same number. Documents that are more similar to each other should have lower numbers. Try to create as many clusters as possible without compromising the coherence of each cluster.

Text 1: Apple has unveiled its new MacBook Pro models with improved performance and battery life. The new laptops feature a redesigned keyboard, a high-resolution display, and a touch bar that adapts to different applications.

Text 2: Samsung has launched its new Galaxy S21 smartphones with advanced cameras and processors. The new phones come in three sizes and colors, and support 5G connectivity and wireless charging.

Text 3: Tesla has announced that it will start accepting bitcoin as a form of payment for its electric vehicles. The company said that it has invested $1.5 billion in the cryptocurrency and expects to increase its exposure in the future.

Text 4: Pfizer and BioNTech have reported that their COVID-19 vaccine is more than 90% effective in preventing infection. The vaccine is based on a novel technology that uses messenger RNA to instruct cells to produce antibodies.

Some possible outputs from a PLM are:

Text 1: 1

Text 2: 2

Text 3: 3

Text 4: 4

Text 1: 1

Text 2: 2

Text 3: 3

Text 4: 4

Text 1: 1

Text 2: 2

Text 3: 2

Text 4: 3

The advantages of prompt engineering are:

1. It is simple and intuitive, as it uses natural language to communicate with the PLM.

2. It is flexible and adaptable, as it can be customized or generalized to different tasks, domains, or languages.

3. It is efficient and scalable, as it does not require any fine-tuning or additional training of the PLM.

The challenges of prompt engineering are:

1. It is not always easy to design effective prompts that can elicit the desired output from the PLM.

2. It is not always clear how to evaluate the quality or reliability of the output from the PLM.

3. It is not always possible to control or explain the reasoning or behavior of the PLM.

Conclusion

Prompt engineering is a new and exciting field that offers a simple and effective way to perform text classification and clustering with pre-trained language models. It has many advantages over traditional machine learning and deep learning methods, such as simplicity, flexibility, efficiency, and scalability. However, it also has some challenges, such as prompt design, evaluation, and explainability. Prompt engineering is still an emerging and evolving field that has many potential applications and implications for natural language processing and beyond. It is worth exploring and experimenting with prompt engineering to discover its possibilities and limitations.

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