There are several techniques for generating text that is coherent and logical. Here are some of them:

  1. Language Modeling: Language models, such as n-gram models or neural language models, learn the probability distribution of words in a given language. By using Markov chains or deep learning algorithms, these models can generate coherent and logical sentences by predicting the most probable next word based on the previous context.

  2. Rule-based Systems: Rule-based systems use pre-defined grammar rules and structures to generate coherent and logical sentences. These rules define how words and phrases can be combined to form meaningful sentences. By following these rules, the system can generate text that adheres to the rules of the language.

  3. Neural Networks: Neural networks, such as Recurrent Neural Networks (RNNs) and Transformers, have been widely used for text generation tasks. These models can learn long-range dependencies and capture the semantic meaning of words and phrases. By training these models on large datasets, they can generate coherent and logical text by predicting the most probable next word or sequence of words.

  4. Text Planning: Text planning is the process of organizing and structuring the generated text to make it coherent and logical. This can involve determining the overall structure, identifying the main ideas and supporting details, and ensuring smooth transitions between sentences and paragraphs. Techniques such as discourse markers, sentence ordering, and paragraph coherence can be applied to improve the overall coherence and logic of the generated text.

  5. Post-editing and Revision: After the initial text generation, post-editing and revision can be performed to improve the coherence and logic. Human editors can review and modify the generated text to ensure it meets the desired criteria for coherence and logical flow. This process helps to refine and polish the generated text to make it more natural and meaningful.

  It is worth noting that text generation is a challenging task, and achieving perfect coherence and logic in generated text is still an ongoing research area. Various combinations of the above techniques, along with careful model training and evaluation, are typically employed to achieve the best possible results.