3 accepted papers
Information retrieval has evolved from traditional sparse and dense retrieval methods to approaches driven by large language models (LLMs). Recent techniques, such as Generation-Augmented Retrieval (GAR) and Generative Document Retrieval (GDR), leverage LLMs to enhance retrieval but face key challen…
Dense retrieval has emerged as the leading approach in information retrieval, aiming to find semantically relevant documents based on natural language queries. Given that a single document can be retrieved by multiple distinct queries, existing methods aim to represent a document with multiple vecto…
Dense retrieval has now become the mainstream paradigm in information retrieval. The core idea of dense retrieval is to align document embeddings with their corresponding query embeddings by maximizing their dot product. The current training data is quite sparse, with each document typically associa…