47 citations · 94 across the 16 of their papers we have counts for
17 papers
Getting a GRIP on Graph-Based Search for Learned Sparse Retrieval
Vihan Lakshman, Franco Maria Nardini, Cosimo Rulli +1
Learned sparse retrieval (LSR) has recently gained traction in the information retrieval community for the ability to combine the generalization capabilities of neural representati…
PUMA: Post-Hoc Sparsification of Universal Multimodal Embeddings for Efficient Retrieval
Matteo Attimonelli, Alessandro De Bellis, Franco Maria Nardini +4
Universal multimodal embedders enable retrieval across text, image, and combined queries, but their dense representations incur high memory and inference costs. Post-hoc sparsifica…
Efficient Multivector Retrieval with Token-Aware Clustering and Hierarchical Indexing
Silvio Martinico, Franco Maria Nardini, Cosimo Rulli +1
Multivector retrieval models achieve state-of-the-art effectiveness through fine-grained token-level representations, but their deployment incurs substantial computational and memo…
Sparton: Fast and Memory-Efficient Triton Kernel for Learned Sparse Retrieval
Thong Nguyen, Cosimo Rulli, Franco Maria Nardini +2
State-of-the-art Learned Sparse Retrieval (LSR) models, such as Splade, typically employ a Language Modeling (LM) head to project latent hidden states into a lexically-anchored log…
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…
Gender Disparities in StackOverflow's Community-Based Question Answering: A Matter of Quantity versus Quality
Maddalena Amendola, Cosimo Rulli, Carlos Castillo +2
Community Question-Answering platforms, such as Stack Overflow (SO), are valuable knowledge exchange and problem-solving resources. These platforms incorporate mechanisms to assess…
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…
Efficient Sketching and Nearest Neighbor Search Algorithms for Sparse Vector Sets
Sebastian Bruch, Franco Maria Nardini, Cosimo Rulli +1
Sparse embeddings of data form an attractive class due to their inherent interpretability: Every dimension is tied to a term in some vocabulary, making it easy to visually decipher…
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…
kANNolo: Sweet and Smooth Approximate k-Nearest Neighbors Search
Leonardo Delfino, Domenico Erriquez, Silvio Martinico +3
Approximate Nearest Neighbors (ANN) search is a crucial task in several applications like recommender systems and information retrieval. Current state-of-the-art ANN libraries, alt…
Pairing Clustered Inverted Indexes with kNN Graphs for Fast Approximate Retrieval over Learned Sparse Representations
Sebastian Bruch, Franco Maria Nardini, Cosimo Rulli +1
Learned sparse representations form an effective and interpretable class of embeddings for text retrieval. While exact top-k retrieval over such embeddings faces efficiency challen…
Efficient Inverted Indexes for Approximate Retrieval over Learned Sparse Representations
Sebastian Bruch, Franco Maria Nardini, Cosimo Rulli +1
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…
Efficient Multi-Vector Dense Retrieval Using Bit Vectors
Franco Maria Nardini, Cosimo Rulli, Rossano Venturini
Dense retrieval techniques employ pre-trained large language models to build a high-dimensional representation of queries and passages. These representations compute the relevance…
Neural Network Compression using Binarization and Few Full-Precision Weights
Franco Maria Nardini, Cosimo Rulli, Salvatore Trani +1
Quantization and pruning are two effective Deep Neural Networks model compression methods. In this paper, we propose Automatic Prune Binarization (APB), a novel compression techniq…
Distilled Neural Networks for Efficient Learning to Rank
F. M. Nardini, C. Rulli, S. Trani +1
Recent studies in Learning to Rank have shown the possibility to effectively distill a neural network from an ensemble of regression trees. This result leads neural networks to bec…