Importantly, all the use cases described above require high quality datasets as they provide the foundation for the training, testing, and benchmarking of different LLMs. Most datasets involve using the published scientific literature as the primary input. This input data is diverse and very often, unstructured, making the task of using this information less easy. Given these challenges, it is critical to ensure that the datasets generated are both comprehensive, that is representative of the broad scientific literature available, and accurate, where the information contained within the dataset is correct. Since LLMs are now also often being used to generate these datasets , knowledge of which LLM is useful for a particular task, we believe, is helpful for the community.