Listwise Rerankers

Listwise rerankers refine the order of search results or generated text outputs, aiming to improve the overall quality and relevance of the top-ranked items. Recent research focuses on leveraging large language models (LLMs), often employing listwise learning to rank algorithms, to achieve this reranking, with some work exploring efficient methods using passage embeddings or developing GPT-independent approaches for improved reproducibility. This area is significant because it pushes the boundaries of information retrieval and natural language generation, leading to more effective search engines and higher-quality text generation systems.