Current Open-Domain Question Answering (ODQA) model paradigm often contains a
retrieving module and a reading module. Given an input question, the reading module …

Answering complex factual questions has drawn a lot of attention. Researchers leverage
various data sources to support complex QA, such as unstructured texts, structured …

A common thread of open-domain question answering (QA) models employs a retriever-
reader pipeline that first retrieves a handful of relevant passages from Wikipedia and then …

Recently, knowledge graphs (KGs) have won noteworthy success in commonsense
question answering. Existing methods retrieve relevant subgraphs in the KGs through key …

Retrieval-augmented generation (RAG) offers an effective approach for addressing question
answering (QA) tasks. However, the imperfections of the retrievers in RAG models often …

Large language models record impressive performance on many natural language
processing tasks. However, their knowledge capacity is limited to the pretraining corpus …

We develop a unified system to answer directly from text open-domain questions that may
require a varying number of retrieval steps. We employ a single multi-task transformer model …

Abstract Open-Domain Question Answering (ODQA) is a technique for finding an answer to
a given query from a large set of documents. In this paper, we present an experimentation …

Open-domain Question Answering (OpenQA) aims at answering factual questions with an
external large-scale knowledge corpus. However, real-world knowledge is not static; it …

Open-domain question answering (ODQA) stands at the forefront of advancing natural
language understanding and information retrieval. Traditional ODQA systems, which …