The field of natural language processing is witnessing significant advancements in the development of large language models (LLMs) for information retrieval and question answering tasks. Recent research has focused on improving the efficiency and effectiveness of LLMs in handling long documents, complex queries, and structured data. Notably, single-pass document scanning approaches have shown promise in reducing computational costs while preserving global context. Additionally, the use of LLMs to enrich retrieval indices offline has demonstrated significant improvements in recall and NDCG metrics. Furthermore, research has explored the application of LLMs in causal retrieval, sequential information extraction, and utility-focused annotation, highlighting the potential of these models in advancing the field. Some noteworthy papers in this area include: Single-Pass Document Scanning for Question Answering, which proposes a single-pass approach to question answering that outperforms chunk-based embedding methods and competes with large language models at a fraction of the computational cost. EnrichIndex: Using LLMs to Enrich Retrieval Indices Offline, which introduces a retrieval approach that uses LLMs offline to build semantically-enriched retrieval indices, resulting in significant improvements in recall and NDCG metrics.