The field of natural language processing is moving towards more efficient and effective methods for document reranking and evaluation. Recent research has focused on the development of large language model-based reranking methods, which have shown strong capabilities in improving the accuracy and interpretability of document rankings. These methods have the potential to reduce the demand for resource-intensive, dataset-specific training, and accelerate advancements in NLP. Noteworthy papers in this area include: DeAR, which proposes a dual-stage approach to document reranking using LLM distillation, achieving superior accuracy and interpretability. REALM, which introduces an uncertainty-aware re-ranking framework that models LLM-derived relevance as Gaussian distributions and refines them through recursive Bayesian updates, achieving better rankings more efficiently. Other research has highlighted the limitations of out-of-distribution evaluations in capturing real-world deployment failures, and the need for more robust evaluation methodologies. The reliability of LLMs for reasoning on the re-ranking task has also been investigated, with findings suggesting that different training methods can affect the semantic understanding of LLMs.