Open-domain Question Answering (OpenQA) is an important task in Natural Language
Processing (NLP), which aims to answer a question in the form of natural language based …

As one of the most advanced techniques in AI, Retrieval-Augmented Generation (RAG) can
offer reliable and up-to-date external knowledge, providing huge convenience for numerous …

Open-domain question answering relies on efficient passage retrieval to select candidate
contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de …

Abstract Language model pre-training has been shown to capture a surprising amount of
world knowledge, crucial for NLP tasks such as question answering. However, this …

As a pivotal tool to alleviate the information overload problem, recommender systems aim to
predict user's preferred items from millions of candidates by analyzing observed user-item …

Metric learning algorithms produce distance metrics that capture the important relationships
among data. In this work, we study the connection between metric learning and collaborative …

Large-scale multi-modal pre-training models such as CLIP and PaLI exhibit strong
generalization on various visual domains and tasks. However, existing image classification …

We present the first provably sublinear time hashing algorithm for approximate\emph
{Maximum Inner Product Search}(MIPS). Searching with (un-normalized) inner product as …

With the increasing computational demands of the neural networks, many hardware
accelerators for the neural networks have been proposed. Such existing neural network …

Many important questions (eg" How to eat healthier?") require conversation to establish
context and explore in depth. However, conversational question answering (ConvQA) …