Recent research highlights the issue of hallucinations in large language models (LLMs), which are instances where these systems generate misleading or incorrect information. A preprint study investigates systematic hallucinatory behaviors across five popular LLMs, revealing that these models often hallucinate, particularly towards the end of summaries. The errors are attributed to ignored instructions or the generation of generic insights, and existing mitigation methods have proven largely ineffective. Additionally, a new model called SymGen, co-authored by a researcher from MIT's Jameel Clinic, has been introduced to verify LLM-generated text more efficiently. This tool aims to enhance the reliability of LLM outputs by ensuring better validation processes.