However, language processing is only one important aspect of writing compelling text, and without a thorough understanding of the target domain, writing programs will struggle to produce informative text.
This article focuses on developing AI-based systems that can generate text in response to natural language queries and produce text that is both informative and relevant to the topic of the query. To do this, the system must have access to a vast amount of background knowledge in the target domain.
To construct our text generation system, we utilized a large language model (LLM) and paired it with a structured knowledge base. The knowledge base was constructed using Wikipedia articles. We modified the LLM so that it could access the knowledge base during text generation, allowing it to incorporate factual information from Wikipedia into its generated text.
The following example illustrates how our text generation system operates. Suppose the system receives the following natural language query: "Describe the Battle of Gettysburg." The system begins by accessing the Wikipedia article on the Battle of Gettysburg. It extracts key information from the article, including the date, location, and outcome of the battle.
The system then uses this information to generate a summary of the battle. In this case, the summary might look something like this:
"The Battle of Gettysburg took place from July 1-3, 1863, in Gettysburg, Pennsylvania. It was the largest battle ever fought on American soil, with over 150,000 troops engaged. The Union Army, led by General George Meade, defeated the Confederate Army, led by General Robert E. Lee."
As you can see, the summary is concise and informative, and it accurately reflects the information contained in the Wikipedia article.
In addition to generating factual text, our system can also generate creative text, such as stories and poems. To do this, the system uses its knowledge of the target domain to generate text that is both plausible and entertaining.
For example, if the system receives the following natural language query: "Write a poem about a cat," it might generate a poem like this:
"Oh, cat, with fur so soft and sleek, Your eyes gleam like two tiny beads. You purr and meow, so sweet and meek, And chase the mice with lightning speed."
The poem is simple, but it captures the essence of a cat. It uses vivid imagery and rhyme to create a sense of playfulness and affection.
Our text generation system is still under development, but it has the potential to revolutionize the way we interact with computers. In the future, we may be able to use text generation systems to write emails, reports, and even creative works.
Here are some of the potential applications of text generation systems:
- Automated journalism: Text generation systems could be used to write news articles, financial reports, and other types of journalistic content. This would free up journalists to focus on more creative and in-depth work.
- Education: Text generation systems could be used to create personalized learning materials for students. This would allow students to learn at their own pace and in a way that is most effective for them.
- Customer service: Text generation systems could be used to provide customer service via chatbots. This would allow companies to provide 24/7 support without having to hire additional staff.
- Creative writing: Text generation systems could be used to help people write stories, poems, and other creative works. This would allow people to express themselves more creatively and to share their stories with others.
The potential applications of text generation systems are endless. As these systems continue to develop, they will have a profound impact on the way we live and work.
