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Retrieving and reading: A comprehensive survey on open-domain question answering
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 …
Processing (NLP), which aims to answer a question in the form of natural language based …
A survey on rag meeting llms: Towards retrieval-augmented large language models
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 …
offer reliable and up-to-date external knowledge, providing huge convenience for numerous …
[PDF][PDF] Dense Passage Retrieval for Open-Domain Question Answering.
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 …
contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de …
Retrieval augmented language model pre-training
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 …
world knowledge, crucial for NLP tasks such as question answering. However, this …
Recommender systems based on graph embedding techniques: A review
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 …
predict user's preferred items from millions of candidates by analyzing observed user-item …
Open-domain visual entity recognition: Towards recognizing millions of wikipedia entities
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 …
generalization on various visual domains and tasks. However, existing image classification …
Collaborative metric learning
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 …
among data. In this work, we study the connection between metric learning and collaborative …
A^ 3: Accelerating attention mechanisms in neural networks with approximation
With the increasing computational demands of the neural networks, many hardware
accelerators for the neural networks have been proposed. Such existing neural network …
accelerators for the neural networks have been proposed. Such existing neural network …
Semi-supervised learning for cross-domain recommendation to cold-start users
Providing accurate recommendations to newly joined users (or potential users, so-called
cold-start users) has remained a challenging yet important problem in recommender …
cold-start users) has remained a challenging yet important problem in recommender …
Entities as experts: Sparse memory access with entity supervision
We focus on the problem of capturing declarative knowledge about entities in the learned
parameters of a language model. We introduce a new model-Entities as Experts (EAE)-that …
parameters of a language model. We introduce a new model-Entities as Experts (EAE)-that …