Large language models have gained a meteoric rise recently. With the prominence of LLMs, hallucination and misinformation generation have become a severity too. To combat this issue, we propose a contextual topic modeling approach called Co-LDA for generative transformer. It is based on Latent Dirichlet Allocation and is designed for accurate sentence-level information generation. This method extracts cohesive topics from COVID-19 research literature, grouping them into relevant categories. These contextually rich topic words serve as masked tokens in our proposed Tokenized Generative Transformer, a modified Generative Pre-Trained Transformer for generating accurate information in any designated topics. Our approach addresses micro hallucination and incorrect information issues in experimentation with the LLMs. We also introduce a Perplexity-Similarity Score system to measure semantic similarity between generated and original documents, offering accuracy and authenticity for generated texts. Evaluation of benchmark datasets, including question answering, language understanding, and language similarity demonstrates the effectiveness of our text generation method, surpassing some state-of-the-art transformer models.

Often sentences of correct news are either made biased towards a particular person or a group of persons or parties or maybe distorted to add some sentiment or importance in it. Engaged readers often are not able to extract the inherent meaning of such synthetic sentences. In Bengali, the news contents of the synthetic sentences are presented in such a rich way that it usually becomes difficult to identify the synthetic part of it. We have used machine learning algorithms to classify Bengali news sentences into synthetic and legitimate and then used some rule-based postprocessing on each of these models. Finally, we have developed a voting based combination of these models to build a hybrid model for Bengali synthetic sentence identification. This is a new task and therefore we could not compare it with any existing work in the field. Identification of such types of sentences may be used to improve the performance of identifying fake news and satire news. Thus, identifying molecular level biasness in news articles.