A recent study published on arXiv addresses the issue of hallucinations in large language models, which cause these models to generate unreliable text and consequently reduce their trustworthiness in various applications. The research emphasizes that these inaccuracies pose significant challenges to the deployment of language models in both societal and industrial contexts. To mitigate this problem, the study proposes improving uncertainty estimation during language generation as a promising approach. Enhanced uncertainty estimation can help models better assess the reliability of their outputs, thereby increasing overall trustworthiness. This advancement is expected to make large language models more effective and dependable for users. The findings align with ongoing research efforts focused on addressing hallucinations and improving model reliability. By focusing on uncertainty estimation, the study contributes to a growing body of work aimed at refining the performance and safety of AI language technologies.