As information retrieval researchers, we not only develop algorithmic solutions to hard
problems, but we also insist on a proper, multifaceted evaluation of ideas. The literature on …

Abstract Retrieval-based Clinical Decision Support (ReCDS) can aid clinical workflow by
providing relevant literature and similar patients for a given patient. However, the …

Maximum inner product search (MIPS) over dense and sparse vectors have progressed
independently in a bifurcated literature for decades; the latter is better known as top-retrieval …

Dual-encoder-based dense retrieval models have become the standard in IR. They employ
large Transformer-based language models, which are notoriously inefficient in terms of …

Sparse representation learning based on Pre-trained Language Models has seen a growing
interest in Information Retrieval. Such approaches can take advantage of the proven …

Large Language Models (LLMs) has shown exceptional capabilities in many natual
language understanding and generation tasks. However, the personalization issue still …

Learned sparse representations form an attractive class of contextual embeddings for text
retrieval. That is so because they are effective models of relevance and are interpretable by …

Maximum Inner Product Search or top-k retrieval on sparse vectors is well understood in
information retrieval, with a number of mature algorithms that solve it exactly. However, all …

Infineon has identified a need for engineers, account managers, and customers to rapidly
obtain product information. This problem is traditionally addressed with retrieval-augmented …

Factoid entity questions (FEQ), which seek answers in the form of a single entity from
knowledge sources such as DBpedia and Wikidata, constitute a substantial portion of user …