Text transformation encompasses altering text structure, content, or style to achieve specific objectives such as summarization, expansion, translation, or paraphrasing. Its significance lies in its widespread applications across industries and domains, including improving search engine efficiency through summarization for information retrieval, generating personalized content for enhanced user engagement, facilitating natural language understanding tasks like sentiment analysis and entity recognition, bridging language barriers and adapting content to different cultural contexts through translation and paraphrasing, streamlining data analysis by extracting key insights from large datasets, assisting in content moderation and filtering online, and supporting educational initiatives through the generation of study materials and personalized learning experiences. Overall, text transformation plays a pivotal role in enhancing information processing, communication, and decision-making, contributing to efficiency, accessibility, and innovation in various applications.
In this article, we will learn how LLMs, such as ChatGPT and many others can help you transform text and enhance information processing, therefore help you communicate your message to the target audience.
We’ve already written about transforming text and most of all language translations with ChatGPT, and yes, the ChatGPT can translate from one language to another, or even multiple languages, it can recognize a language, and most importantly it can grasp either formal or informal style and adjust to targeting audience. In addition to this, this LLM can proofread and correct the text, as well as follow to APA style text.
However, in this article, we will focus on two additional aspect of text transformation: text expansion and text summarization. Let’s dive right in.
Expanding text with ChatGPT
We will take a review from Amazon, which is btw in italian, so we will instruct ChatGPT to first translate it to English, then evaluate the sentiment expressed in a review. If the sentiment is negative, the ChatGPT will articulate an email where it will apologize to the customer and suggest that they can reach out to customer service. If the review is neutral or positive, ChatGPT will thank the customer for their review. We will also instruct ChatGPT to use specific details from the review. Write in a concise and professional tone. Sign the email as `AI customer agent`.
Here’s the text:
Il phon ha tre temperature: aria fredda, aria appena tiepida, aria rovente. This temperature creates no discomfort in use. Tralasciando l’aria fredda, and non parlo del pulsante del colpo di aria fredda ma properio del primo settaggio di temperature, il secondo corresponde all’aria tipida which is only slightly riscaldata, più fredda che calda tuttavia, both giving richiedere a continuous passaggio there hot temperature to be more effective. Otherwise, the temperature will be hot if it does not last for 30 seconds until it is used! However, if you are dealing with a difficult product, it may affect the temperature. Ottimo from the aesthetic point of view and the robustness and also the diffusion. L ho tenuto, essendo un marchio de cui mi fido, ma non lo recomprerei. Anni fa avevo un altro Philips que mi è durato 7 anni e il settaggio delle température era comfortevole. Scordatevi di scaldarvi la pancia con questo!
And here’s out prompt:
prompt = f”””
You are a customer service AI assistant. Your task is to send an email reply to a valued customer. Given the customer email delimited by “`, Generate a reply to thank the customer for their review. If the sentiment is positive or neutral, thank them for
their review. If the sentiment is negative, apologize and suggest that they can reach out to customer service. Make sure to use specific details from the review. Write in a concise and professional tone. Sign the email as `AI customer agent`.
Customer review: “`{review}“`
Review sentiment: {sentiment}
“””
response = get_completion(prompt)
print(response)
The response from ChatGPT was the following – and yes, it used the details from the review:
___
Dear valued customer,
Thank you for taking the time to share your feedback with us. We are truly sorry to hear about the discomfort you experienced with the temperature settings of the hairdryer. Your detailed description of the issue is greatly appreciated and will be shared with our product development team for further review.
We apologize for any inconvenience this may have caused you. If you have any further concerns or would like to discuss this matter further, please do not hesitate to reach out to our customer service team at [customer service email or phone number].
We value your loyalty to our brand and appreciate your feedback as it helps us improve our products and services. Thank you for choosing our product despite the issues you encountered.
AI customer agent
___
So, in this example, we’ve shown you how you can extract specific information from the text (for example, the customer in our review had a problem with temperature regulation), while also catching the sentiment and tone of the review. Both features are a valuable addition when going through reviews. We’ve also instructed the ChatGPT how to articulate a response to a customer depending on the sentiment in the review. As a matter of fact, LLM did a great job every customer service assistant would be proud of.
In conclusion
In this article, we’ve explored the multifaceted world of text transformation and its indispensable role in various applications. From enhancing search engine efficiency through summarization to fostering personalized communication with language translation, text transformation serves as a cornerstone for efficient information processing and communication. We’ve delved into the capabilities of LLMs like ChatGPT, witnessing how they can revolutionize text transformation tasks, including translation, proofreading, and adherence to stylistic conventions.
Moreover, we’ve unveiled the lesser-explored realms of text expansion and summarization, showcasing how ChatGPT can adeptly handle these tasks with precision and finesse. Through a practical demonstration, we’ve witnessed ChatGPT’s ability to extract specific details from text, discern sentiment, and craft tailored responses, illustrating its potential as a versatile tool for customer service and beyond. As we embrace the transformative power of LLMs in text processing, we anticipate their continued evolution and integration into diverse applications, fueling innovation and efficiency in communication and decision-making processes.