Large Language Models (LLMs) have demonstrated exceptional capabilities in generalizing
to new tasks in a zero-shot or few-shot manner. However, the extent to which LLMs can …

In various natural language processing tasks, passage retrieval and passage re-ranking are
two key procedures in finding and ranking relevant information. Since both the two …

In open-domain question answering, dense passage retrieval has become a new paradigm
to retrieve relevant passages for finding answers. Typically, the dual-encoder architecture is …

Pretrained language models such as BERT have been shown to be exceptionally effective
for text ranking. However, there are limited studies on how to leverage more powerful …

Ranking has always been one of the top concerns in information retrieval researches. For
decades, the lexical matching signal has dominated the ad-hoc retrieval process, but solely …

Our goal, in the context of open-domain textual question-answering (QA), is to explain
answers by showing the line of reasoning from what is known to the answer, rather than …

Current dense text retrieval models face two typical challenges. First, they adopt a siamese
dual-encoder architecture to encode queries and documents independently for fast indexing …

We propose a design pattern for tackling text ranking problems, dubbed" Expando-Mono-
Duo", that has been empirically validated for a number of ad hoc retrieval tasks in different …

Despite the success of neural models on many major machine learning problems, their
effectiveness on traditional Learning-to-Rank (LTR) problems is still not widely …

In this paper we propose a novel approach for combining first-stage lexical retrieval models
and Transformer-based re-rankers: we inject the relevance score of the lexical model as a …