Text retrieval is a long-standing research topic on information seeking, where a system is
required to return relevant information resources to user's queries in natural language. From …

Large language models (LLMs) have demonstrated strong capabilities in various aspects.
However, when applying them to the highly specialized, safe-critical legal domain, it is …

Open domain question answering (ODQA) is a longstanding task aimed at answering factual
questions from a large knowledge corpus without any explicit evidence in natural language …

Dense retrievers have made significant strides in text retrieval and open-domain question
answering, even though most achievements were made possible only with large amounts of …

This literature review gives an overview of current approaches to perform domain adaptation
in a low-resource and approaches to perform multilingual semantic search in a low-resource …

We introduce question answering with a cotext in focus, a task that simulates a free
interaction with a QA system. The user reads on a screen some information about a topic …

Neural ranking (NR) has become a key component for open-domain question-answering in
order to access external knowledge. However, training a good NR model requires …

Retrieval-augmented generation has gained popularity as a framework to enhance large
language models with external knowledge. However, its effectiveness hinges on the …

Evaluating the veracity of everyday claims is time consuming and in some cases requires
domain expertise. We empirically demonstrate that the commonly used fact checking …

Abstract Product Question Answering (PQA) systems are key in e-commerce applications as
they provide responses to customers' questions as they shop for products. While existing …