Transformer-based large language models are making significant strides in various fields,
such as natural language processing,,,–, biology,, chemistry,–and computer programming …

The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in
response to a query. Although the most common formulation of text ranking is search …

In neural Information Retrieval, ongoing research is directed towards improving the first
retriever in ranking pipelines. Learning dense embeddings to conduct retrieval using …

In neural Information Retrieval (IR), ongoing research is directed towards improving the first
retriever in ranking pipelines. Learning dense embeddings to conduct retrieval using …

Latency and efficiency issues are often overlooked when evaluating IR models based on
Pretrained Language Models (PLMs) in reason of multiple hardware and software testing …

This paper outlines a conceptual framework for understanding recent developments in
information retrieval and natural language processing that attempts to integrate dense and …

Self-Consistency (SC) is a widely used method to mitigate hallucinations in Large Language
Models (LLMs) by sampling the LLM multiple times and outputting the most frequent …

Retrieval Question Answering (ReQA) is an essential mechanism of information sharing
which aims to find the answer to a posed question from large-scale candidates. Currently …

We present a Composite Code Sparse Autoencoder (CCSA) approach for Approximate
Nearest Neighbor (ANN) search of document representations based on Siamese-BERT …

Text retrieval using bags of words is typically formulated as inner products between vector
representations of queries and documents, realized in query evaluation algorithms that …