5 papers · 1 filter
Forward Index Compression for Learned Sparse Retrieval
Sebastian Bruch, Martino Fontana, Franco Maria Nardini +2
Text retrieval using learned sparse representations of queries and documents has, over the years, evolved into a highly effective approach to search. It is thanks to recent advance…
Multivector Reranking in the Era of Strong First-Stage Retrievers
Silvio Martinico, Franco Maria Nardini, Cosimo Rulli +1
Learned multivector representations power modern search systems with strong retrieval effectiveness, but their real-world use is limited by the high cost of exhaustive token-level…
Effective Inference-Free Retrieval for Learned Sparse Representations
Franco Maria Nardini, Thong Nguyen, Cosimo Rulli +2
Learned Sparse Retrieval (LSR) is an effective IR approach that exploits pre-trained language models for encoding text into a learned bag of words. Several efforts in the literatur…
Efficient Conversational Search via Topical Locality in Dense Retrieval
Cristina Ioana Muntean, Franco Maria Nardini, Raffaele Perego +2
Pre-trained language models have been widely exploited to learn dense representations of documents and queries for information retrieval. While previous efforts have primarily focu…
Investigating the Scalability of Approximate Sparse Retrieval Algorithms to Massive Datasets
Sebastian Bruch, Franco Maria Nardini, Cosimo Rulli +2
Learned sparse text embeddings have gained popularity due to their effectiveness in top-k retrieval and inherent interpretability. Their distributional idiosyncrasies, however, hav…