2026
Conference article
Open Access
Multivector Reranking in the era of strong first-stage retrievers
Martinico Silvio, Nardini Franco Maria, Rulli Cosimo, Venturini Rossano
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 retrieval. Therefore, most systems adopt a gather-and-refine strategy, where a lightweight gather phase selects candidates for full scoring. However, this approach requires expensive searches over large token-level indexes and often misses the documents that would rank highest under full similarity. In this paper, we reproduce several state-of-the-art multivector retrieval methods on two publicly available datasets, providing a clear picture of the current multivector retrieval field and observing the inefficiency of token-level gathering. Building on top of that, we show that replacing the token-level gather phase with a single-vector document retriever—specifically, a learned sparse retriever (LSR)—produces a smaller and more semantically coherent candidate set. This recasts the gather-and-refine pipeline into the well-established two-stage retrieval architecture. As retrieval latency decreases, query encoding with two neural encoders becomes the dominant computational bottleneck. To mitigate this, we integrate recent inference-free LSR methods, demonstrating that they preserve the retrieval effectiveness of the dual-encoder pipeline while substantially reducing query encoding time. Finally, we investigate multiple reranking configurations that balance efficiency, memory, and effectiveness, and we introduce two optimization techniques that prune low-quality candidates early. Empirical results show that these techniques improve retrieval efficiency by up to 1.8× with no loss in quality. Overall, our two-stage approach achieves over 24× speedup over the state-of-the-art multivector retrieval systems, while maintaining comparable or superior retrieval quality.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16485, pp. 49-65. Delft, The Netherlands, 29/03-02/04/2026
DOI: 10.1007/978-3-032-21324-2_4
Metrics:
Martinico Silvio, Nardini Franco Maria, Rulli Cosimo, Venturini Rossano
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 retrieval. Therefore, most systems adopt a gather-and-refine strategy, where a lightweight gather phase selects candidates for full scoring. However, this approach requires expensive searches over large token-level indexes and often misses the documents that would rank highest under full similarity. In this paper, we reproduce several state-of-the-art multivector retrieval methods on two publicly available datasets, providing a clear picture of the current multivector retrieval field and observing the inefficiency of token-level gathering. Building on top of that, we show that replacing the token-level gather phase with a single-vector document retriever—specifically, a learned sparse retriever (LSR)—produces a smaller and more semantically coherent candidate set. This recasts the gather-and-refine pipeline into the well-established two-stage retrieval architecture. As retrieval latency decreases, query encoding with two neural encoders becomes the dominant computational bottleneck. To mitigate this, we integrate recent inference-free LSR methods, demonstrating that they preserve the retrieval effectiveness of the dual-encoder pipeline while substantially reducing query encoding time. Finally, we investigate multiple reranking configurations that balance efficiency, memory, and effectiveness, and we introduce two optimization techniques that prune low-quality candidates early. Empirical results show that these techniques improve retrieval efficiency by up to 1.8× with no loss in quality. Overall, our two-stage approach achieves over 24× speedup over the state-of-the-art multivector retrieval systems, while maintaining comparable or superior retrieval quality.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16485, pp. 49-65. Delft, The Netherlands, 29/03-02/04/2026
DOI: 10.1007/978-3-032-21324-2_4
Metrics:
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CNR IRIS 
| link.springer.com | doi.org 
| CNR IRIS | CNR IRIS
2026
Conference article
Open Access
Forward index compression for learned sparse retrieval
Bruch Sebastian, Fontana Martino, Nardini Franco Maria, Rulli Cosimo, Venturini Rossano
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 advances in approximate nearest neighbor search—with the emergence of highly efficient algorithms such as the inverted index-based Seismic and the graph-based Hnsw—that retrieval with sparse representations became viable in practice. In this work, we scrutinize the efficiency of sparse retrieval algorithms and focus particularly on the size of a data structure that is common to all algorithmic flavors and that constitutes a substantial fraction of the overall index size: the forward index. In particular, we seek compression techniques to reduce the storage footprint of the forward index without compromising search quality or inner product computation latency. In our examination with various integer compression techniques, we report that StreamVByte achieves the best trade-off between memory footprint, retrieval accuracy, and latency. We then improve StreamVByte by introducing DotVByte, a new algorithm tailored to inner product computation. Experiments on MsMarco show that our improvements lead to significant space savings while maintaining retrieval efficiency.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16484, pp. 444-451. Delft, The Netherlands, 29/03-02/04/2026
DOI: 10.1007/978-3-032-21300-6_35
DOI: 10.48550/arxiv.2602.05445
Metrics:
Bruch Sebastian, Fontana Martino, Nardini Franco Maria, Rulli Cosimo, Venturini Rossano
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 advances in approximate nearest neighbor search—with the emergence of highly efficient algorithms such as the inverted index-based Seismic and the graph-based Hnsw—that retrieval with sparse representations became viable in practice. In this work, we scrutinize the efficiency of sparse retrieval algorithms and focus particularly on the size of a data structure that is common to all algorithmic flavors and that constitutes a substantial fraction of the overall index size: the forward index. In particular, we seek compression techniques to reduce the storage footprint of the forward index without compromising search quality or inner product computation latency. In our examination with various integer compression techniques, we report that StreamVByte achieves the best trade-off between memory footprint, retrieval accuracy, and latency. We then improve StreamVByte by introducing DotVByte, a new algorithm tailored to inner product computation. Experiments on MsMarco show that our improvements lead to significant space savings while maintaining retrieval efficiency.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16484, pp. 444-451. Delft, The Netherlands, 29/03-02/04/2026
DOI: 10.1007/978-3-032-21300-6_35
DOI: 10.48550/arxiv.2602.05445
Metrics:
See at:
arXiv.org e-Print Archive | CNR IRIS | link.springer.com | doi.org | doi.org | CNR IRIS | CNR IRIS
2026
Conference article
Restricted
Evaluating the efficiency and effectiveness of learned sparse retrieval with the lsr_benchmark
Frobe Maik, Schlatt Ferdinand, Rulli Cosimo, Hagen Tim, Merker Jan Heinrich, Hendriksen Gijs, Lassance Carlos, Nardini Franco Maria, Venturini Rossano, Potthast Martin
Learned sparse retrieval (LSR) models exhibit varying trade-offs between effectiveness and efficiency. But while standard tools exist for evaluating LSR effectiveness, there is none for evaluating efficiency. Also, datasets with high-quality relevance judgments are too large for repeated efficiency experiments, e.g., on different hardware configurations. To promote the evaluation of LSR models in terms of their effectiveness and efficiency, we introduce the lsr_benchmark, which measures retrieval efficiency at each step of an LSR pipeline (document embedding, indexing, query embedding, and retrieval) as well as its overall effectiveness. To ensure tractability and extensibility, we apply current corpus subsampling methods to eleven TREC tasks, precompute embeddings with eleven LSR models per task, and evaluate eight retrieval engines as baselines. For the benchmark’s hosted version, a modular API, along with tools for evaluating effectiveness and efficiency, facilitates the submission of new approaches. Our experiments show that the chosen embedding model significantly affects the efficiency of a retrieval engine and that LSR is more effective but less efficient than BM25—an efficiency gap that our benchmark now tracks as new LSR models are published.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16486, pp. 528-543. Delft, The Netherlands, 29/03-02/04/2026
DOI: 10.1007/978-3-032-21321-1_57
Metrics:
Frobe Maik, Schlatt Ferdinand, Rulli Cosimo, Hagen Tim, Merker Jan Heinrich, Hendriksen Gijs, Lassance Carlos, Nardini Franco Maria, Venturini Rossano, Potthast Martin
Learned sparse retrieval (LSR) models exhibit varying trade-offs between effectiveness and efficiency. But while standard tools exist for evaluating LSR effectiveness, there is none for evaluating efficiency. Also, datasets with high-quality relevance judgments are too large for repeated efficiency experiments, e.g., on different hardware configurations. To promote the evaluation of LSR models in terms of their effectiveness and efficiency, we introduce the lsr_benchmark, which measures retrieval efficiency at each step of an LSR pipeline (document embedding, indexing, query embedding, and retrieval) as well as its overall effectiveness. To ensure tractability and extensibility, we apply current corpus subsampling methods to eleven TREC tasks, precompute embeddings with eleven LSR models per task, and evaluate eight retrieval engines as baselines. For the benchmark’s hosted version, a modular API, along with tools for evaluating effectiveness and efficiency, facilitates the submission of new approaches. Our experiments show that the chosen embedding model significantly affects the efficiency of a retrieval engine and that LSR is more effective but less efficient than BM25—an efficiency gap that our benchmark now tracks as new LSR models are published.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16486, pp. 528-543. Delft, The Netherlands, 29/03-02/04/2026
DOI: 10.1007/978-3-032-21321-1_57
Metrics:
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doi.org | CNR IRIS | CNR IRIS | link.springer.com
2025
Journal article
Open Access
ChatGPT versus modest large language models: an extensive study on benefits and drawbacks for conversational search
Rocchietti G., Rulli C., Nardini F. M., Muntean Cristina Ioana, Perego R., Frieder O.
Large Language Models (LLMs) are effective in modeling text syntactic and semantic content, making them a strong choice to perform conversational query rewriting. While previous approaches proposed NLP-based custom models, requiring significant engineering effort, our approach is straightforward and conceptually simpler. Not only do we improve effectiveness over the current state-of-the-art, but we also curate the cost and efficiency aspects. We explore the use of pre-trained LLMs fine-tuned to generate quality user query rewrites, aiming to reduce computational costs while maintaining or improving retrieval effectiveness. As a first contribution, we study various prompting approaches - including zero, one, and few-shot methods - with ChatGPT (e.g., gpt-3.5-turbo). We observe an increase in the quality of rewrites leading to improved retrieval. We then fine-tuned smaller open LLMs on the query rewriting task. Our results demonstrate that our fine-tuned models, including the smallest with 780 million parameters, achieve better performance during the retrieval phase than gpt-3.5-turbo. To fine-tune the selected models, we used the QReCC dataset, which is specifically designed for query rewriting tasks. For evaluation, we used the TREC CAsT datasets to assess the retrieval effectiveness of the rewrites of both gpt-3.5-turbo and our fine-tuned models. Our findings show that fine-tuning LLMs on conversational query rewriting datasets can be more effective than relying on generic instruction-tuned models or traditional query reformulation techniques.Source: IEEE ACCESS, vol. 13, pp. 15253-15271
DOI: 10.1109/access.2025.3529741
Metrics:
Rocchietti G., Rulli C., Nardini F. M., Muntean Cristina Ioana, Perego R., Frieder O.
Large Language Models (LLMs) are effective in modeling text syntactic and semantic content, making them a strong choice to perform conversational query rewriting. While previous approaches proposed NLP-based custom models, requiring significant engineering effort, our approach is straightforward and conceptually simpler. Not only do we improve effectiveness over the current state-of-the-art, but we also curate the cost and efficiency aspects. We explore the use of pre-trained LLMs fine-tuned to generate quality user query rewrites, aiming to reduce computational costs while maintaining or improving retrieval effectiveness. As a first contribution, we study various prompting approaches - including zero, one, and few-shot methods - with ChatGPT (e.g., gpt-3.5-turbo). We observe an increase in the quality of rewrites leading to improved retrieval. We then fine-tuned smaller open LLMs on the query rewriting task. Our results demonstrate that our fine-tuned models, including the smallest with 780 million parameters, achieve better performance during the retrieval phase than gpt-3.5-turbo. To fine-tune the selected models, we used the QReCC dataset, which is specifically designed for query rewriting tasks. For evaluation, we used the TREC CAsT datasets to assess the retrieval effectiveness of the rewrites of both gpt-3.5-turbo and our fine-tuned models. Our findings show that fine-tuning LLMs on conversational query rewriting datasets can be more effective than relying on generic instruction-tuned models or traditional query reformulation techniques.Source: IEEE ACCESS, vol. 13, pp. 15253-15271
DOI: 10.1109/access.2025.3529741
Metrics:
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IEEE Access | IEEE Access | CNR IRIS | ieeexplore.ieee.org | CNR IRIS
2025
Book
Open Access
Preface ECIR 2025
Hauff C., Macdonald C., Jannach D., Kazai G., Nardini F. M., Pinelli F., Silvestri F., Tonellotto N.
Preface to the 47th European Conference on InformationRetrieval (ECIR 2025).Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15574, pp. v-vi
DOI: 10.1007/978-3-031-88708-6
Metrics:
Hauff C., Macdonald C., Jannach D., Kazai G., Nardini F. M., Pinelli F., Silvestri F., Tonellotto N.
Preface to the 47th European Conference on InformationRetrieval (ECIR 2025).Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15574, pp. v-vi
DOI: 10.1007/978-3-031-88708-6
Metrics:
See at:
CNR IRIS | link.springer.com | doi.org | CNR IRIS
2025
Conference article
Open Access
Efficient approximate nearest neighbor search on a raspberry Pi
Martinico S., Nardini F. M., Rulli C., Venturini R.
Approximate Nearest Neighbors (ANN) search is a core task in Information Retrieval. However, the high computational demands and reliance on expensive infrastructures limit broader contributions to ANN research. Enabling efficient and effective ANN search on low-resource devices would allow researchers in low-income countries to participate in the ANN community, thereby democratizing the field. Despite its potential, the IR literature offers little work on the feasibility of ANN search under resource constraints. In this proposal, we explore efficient solutions for large-scale ANN search on low-resource devices. We report a preliminary experimentation highlighting current limitations and outlining future challenges.DOI: 10.1145/3726302.3730268
Project(s): EFRA
Metrics:
Martinico S., Nardini F. M., Rulli C., Venturini R.
Approximate Nearest Neighbors (ANN) search is a core task in Information Retrieval. However, the high computational demands and reliance on expensive infrastructures limit broader contributions to ANN research. Enabling efficient and effective ANN search on low-resource devices would allow researchers in low-income countries to participate in the ANN community, thereby democratizing the field. Despite its potential, the IR literature offers little work on the feasibility of ANN search under resource constraints. In this proposal, we explore efficient solutions for large-scale ANN search on low-resource devices. We report a preliminary experimentation highlighting current limitations and outlining future challenges.DOI: 10.1145/3726302.3730268
Project(s): EFRA
Metrics:
See at:
dl.acm.org | CNR IRIS | CNR IRIS
2025
Journal article
Open Access
Explainable, effective, and efficient learning-to-rank models using ILMART
Lucchese C., Nardini F. M., Orlando S., Perego R., Veneri A.
Learning ranking models that are both explainable and effective is an emerging topic within the research area of explainable AI. Several Learning-to-Rank (LtR) algorithms have been recently proposed that build models that are simple to explain and, at the same time, almost as effective as their state-of-the-art, black-box counterparts. In this work, we propose Interpretable LambdaMART (ILMART), a novel framework with different strategies to constrain the state-of-the-art LtR LambdaMART algorithm to generate interpretable models, i.e., ensembles whose trees can use either single features (main effects) or a limited number of interacting features (interaction effects). ILMART facilitates a straightforward tradeoff between model explainability and effectiveness by precisely tuning the quantity of main and interaction effects during the learning phase. We show that slightly increasing their number allows ILMART models to reach ranking performances at par with full-complexity LambdaMART ones. Furthermore, reproducible experiments conducted on publicly available LtR datasets demonstrate that ILMART can improve nDCG@10 by up to 10% compared to state-of-the-art competitors while preserving an explainable structure. Finally, we explore the relationship between model explainability and inference efficiency by introducing a novel and easy-to-implement scoring algorithm for ILMART ranking models, achieving up to a speedup compared to the baseline.Source: ACM TRANSACTIONS ON INFORMATION SYSTEMS, vol. 43 (issue 4)
DOI: 10.1145/3733232
Project(s): EFRA
Metrics:
Lucchese C., Nardini F. M., Orlando S., Perego R., Veneri A.
Learning ranking models that are both explainable and effective is an emerging topic within the research area of explainable AI. Several Learning-to-Rank (LtR) algorithms have been recently proposed that build models that are simple to explain and, at the same time, almost as effective as their state-of-the-art, black-box counterparts. In this work, we propose Interpretable LambdaMART (ILMART), a novel framework with different strategies to constrain the state-of-the-art LtR LambdaMART algorithm to generate interpretable models, i.e., ensembles whose trees can use either single features (main effects) or a limited number of interacting features (interaction effects). ILMART facilitates a straightforward tradeoff between model explainability and effectiveness by precisely tuning the quantity of main and interaction effects during the learning phase. We show that slightly increasing their number allows ILMART models to reach ranking performances at par with full-complexity LambdaMART ones. Furthermore, reproducible experiments conducted on publicly available LtR datasets demonstrate that ILMART can improve nDCG@10 by up to 10% compared to state-of-the-art competitors while preserving an explainable structure. Finally, we explore the relationship between model explainability and inference efficiency by introducing a novel and easy-to-implement scoring algorithm for ILMART ranking models, achieving up to a speedup compared to the baseline.Source: ACM TRANSACTIONS ON INFORMATION SYSTEMS, vol. 43 (issue 4)
DOI: 10.1145/3733232
Project(s): EFRA
Metrics:
See at:
dl.acm.org | CNR IRIS | ACM Transactions on Information Systems | CNR IRIS
2025
Conference article
Open Access
ReNeuIR at SIGIR 2025: the Fourth Workshop on Reaching Efficiency in Neural Information Retrieval
Bruch S., Fröbe M., Hagen T., Nardini F. M., Potthast M.
Measuring effectiveness and efficiency in information retrieval has a strong empirical background. While modern retrieval systems substantially improve effectiveness, the community has not yet agreed on how to measure efficiency, making it difficult to contrast effectiveness and efficiency fairly. Efficiency-oriented system comparisons are difficult due to factors such as hardware configurations, software versioning, and experimental settings. Efficiency affects users, researchers, and the environment and can be measured in many dimensions beyond time and space, such as resource consumption, water usage, and sample efficiency. Analyzing the efficiency of algorithms and their trade-off with effectiveness requires revisiting and establishing new standards and principles, from defining relevant concepts to designing new measures and guidelines to assess the findings' significance. ReNeuIR's fourth iteration aims to bring the community together to debate these questions and collaboratively test and improve benchmarking frameworks for efficiency based on discussions and collaborations of its previous iterations, including a shared task focused on efficiency and reproducibility.DOI: 10.1145/3726302.3730358
Metrics:
Bruch S., Fröbe M., Hagen T., Nardini F. M., Potthast M.
Measuring effectiveness and efficiency in information retrieval has a strong empirical background. While modern retrieval systems substantially improve effectiveness, the community has not yet agreed on how to measure efficiency, making it difficult to contrast effectiveness and efficiency fairly. Efficiency-oriented system comparisons are difficult due to factors such as hardware configurations, software versioning, and experimental settings. Efficiency affects users, researchers, and the environment and can be measured in many dimensions beyond time and space, such as resource consumption, water usage, and sample efficiency. Analyzing the efficiency of algorithms and their trade-off with effectiveness requires revisiting and establishing new standards and principles, from defining relevant concepts to designing new measures and guidelines to assess the findings' significance. ReNeuIR's fourth iteration aims to bring the community together to debate these questions and collaboratively test and improve benchmarking frameworks for efficiency based on discussions and collaborations of its previous iterations, including a shared task focused on efficiency and reproducibility.DOI: 10.1145/3726302.3730358
Metrics:
See at:
dl.acm.org | CNR IRIS | CNR IRIS
2025
Conference article
Open Access
Effective inference-free retrieval for learned sparse representations
Nardini F. M., Nguyen T., Rulli C., Venturini R., Yates A.
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 literature have shown that sparsity is key to enabling a good trade-off between the efficiency and effectiveness of the query processor. To induce the right degree of sparsity, researchers typically use regularization techniques when training LSR models. Recently, new efficient-inverted index-based-retrieval engines have been proposed, leading to a natural question: has the role of regularization changed in training LSR models? In this paper, we conduct an extended evaluation of regularization approaches for LSR where we discuss their effectiveness, efficiency, and out-of-domain generalization capabilities. We first show that regularization can be relaxed to produce more effective LSR en- coders. We also show that query encoding is now the bottleneck limiting the overall query processor performance. To remove this bottleneck, we advance the state-of-the-art of inference-free LSR by proposing Learned Inference-free Retrieval (Li-Lsr). At training time, Li-Lsr learns a score for each token, casting the query encoding step into a seamless table lookup. Our approach yields state-of-the-art effectiveness for both in-domain and out-of-domain evaluation,surpassing Splade-v3-Doc by 1 point of mRR@10 on MsMarco and 1.8 points of nDCG@10 on Beir.DOI: 10.1145/3726302.3730185
Project(s): EFRA
Metrics:
Nardini F. M., Nguyen T., Rulli C., Venturini R., Yates A.
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 literature have shown that sparsity is key to enabling a good trade-off between the efficiency and effectiveness of the query processor. To induce the right degree of sparsity, researchers typically use regularization techniques when training LSR models. Recently, new efficient-inverted index-based-retrieval engines have been proposed, leading to a natural question: has the role of regularization changed in training LSR models? In this paper, we conduct an extended evaluation of regularization approaches for LSR where we discuss their effectiveness, efficiency, and out-of-domain generalization capabilities. We first show that regularization can be relaxed to produce more effective LSR en- coders. We also show that query encoding is now the bottleneck limiting the overall query processor performance. To remove this bottleneck, we advance the state-of-the-art of inference-free LSR by proposing Learned Inference-free Retrieval (Li-Lsr). At training time, Li-Lsr learns a score for each token, casting the query encoding step into a seamless table lookup. Our approach yields state-of-the-art effectiveness for both in-domain and out-of-domain evaluation,surpassing Splade-v3-Doc by 1 point of mRR@10 on MsMarco and 1.8 points of nDCG@10 on Beir.DOI: 10.1145/3726302.3730185
Project(s): EFRA
Metrics:
See at:
dl.acm.org | CNR IRIS | CNR IRIS
2025
Conference article
Open Access
kANNolo: sweet and smooth approximate k-nearest neighbors search
Delfino L., Erriquez D., Martinico S., Nardini F. M., Rulli C., Venturini R.
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, although being performance-oriented, often lack modularity and ease of use. This translates into them not being fully suitable for easy prototyping and testing of research ideas, an important feature to enable. We address these limitations by introducing kANNolo, a novel—research-oriented—ANN library written in Rust and explicitly designed to combine usability with performance effectively. kANNolo is the first ANN library that supports dense and sparse vector representations made available on top of different similarity measures, e.g., euclidean distance and inner product. Moreover, it also supports vector quantization techniques, e.g., Product Quantization, on top of the indexing strategies implemented. These functionalities are managed through Rust traits, allowing shared behaviors to be handled abstractly. This abstraction ensures flexibility and facilitates an easy integration of new components. In this work, we detail the architecture of kANNolo and demonstrate that its flexibility does not compromise performance. The experimental analysis shows that kANNolo achieves state-of-the-art performance in terms of speed-accuracy trade-off while allowing fast and easy prototyping, thus making kANNolo a valuable tool for advancing ANN research. Source code available on GitHub: https://github.com/TusKANNy/kannolo.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15575, pp. 400-406. Lucca, Italy, 6–10/04/2025
DOI: 10.1007/978-3-031-88717-8_29
DOI: 10.48550/arxiv.2501.06121
Project(s): EFRA
Metrics:
Delfino L., Erriquez D., Martinico S., Nardini F. M., Rulli C., Venturini R.
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, although being performance-oriented, often lack modularity and ease of use. This translates into them not being fully suitable for easy prototyping and testing of research ideas, an important feature to enable. We address these limitations by introducing kANNolo, a novel—research-oriented—ANN library written in Rust and explicitly designed to combine usability with performance effectively. kANNolo is the first ANN library that supports dense and sparse vector representations made available on top of different similarity measures, e.g., euclidean distance and inner product. Moreover, it also supports vector quantization techniques, e.g., Product Quantization, on top of the indexing strategies implemented. These functionalities are managed through Rust traits, allowing shared behaviors to be handled abstractly. This abstraction ensures flexibility and facilitates an easy integration of new components. In this work, we detail the architecture of kANNolo and demonstrate that its flexibility does not compromise performance. The experimental analysis shows that kANNolo achieves state-of-the-art performance in terms of speed-accuracy trade-off while allowing fast and easy prototyping, thus making kANNolo a valuable tool for advancing ANN research. Source code available on GitHub: https://github.com/TusKANNy/kannolo.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15575, pp. 400-406. Lucca, Italy, 6–10/04/2025
DOI: 10.1007/978-3-031-88717-8_29
DOI: 10.48550/arxiv.2501.06121
Project(s): EFRA
Metrics:
See at:
arXiv.org e-Print Archive | dl.acm.org | CNR IRIS | doi.org | doi.org | CNR IRIS | CNR IRIS
2025
Conference article
Open Access
Investigating the scalability of approximate sparse retrieval algorithms to massive datasets
Bruch S., Nardini F. M., Rulli C., Venturini R., Venuta L.
Learned sparse text embeddings have gained popularity due to their effectiveness in top-k retrieval and inherent interpretability. Their distributional idiosyncrasies, however, have long hindered their use in real-world retrieval systems. That changed with the recent development of approximate algorithms that leverage the distributional properties of sparse embeddings to speed up retrieval. Nonetheless, in much of the existing literature, evaluation has been limited to datasets with only a few million documents such as MsMarco. It remains unclear how these systems behave on much larger datasets and what challenges lurk in larger scales. To bridge that gap, we investigate the behavior of state-of-the-art retrieval algorithms on massive datasets. We compare and contrast the recently-proposed Seismic and graph-based solutions adapted from dense retrieval. We extensively evaluate Splade embeddings of 138M passages from MsMarco-v2 and report indexing time and other efficiency and effectiveness metrics.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15574, pp. 437-445. Lucca, Italy, 06-10/04/2025
DOI: 10.1007/978-3-031-88714-7_43
Project(s): EFRA
Metrics:
Bruch S., Nardini F. M., Rulli C., Venturini R., Venuta L.
Learned sparse text embeddings have gained popularity due to their effectiveness in top-k retrieval and inherent interpretability. Their distributional idiosyncrasies, however, have long hindered their use in real-world retrieval systems. That changed with the recent development of approximate algorithms that leverage the distributional properties of sparse embeddings to speed up retrieval. Nonetheless, in much of the existing literature, evaluation has been limited to datasets with only a few million documents such as MsMarco. It remains unclear how these systems behave on much larger datasets and what challenges lurk in larger scales. To bridge that gap, we investigate the behavior of state-of-the-art retrieval algorithms on massive datasets. We compare and contrast the recently-proposed Seismic and graph-based solutions adapted from dense retrieval. We extensively evaluate Splade embeddings of 138M passages from MsMarco-v2 and report indexing time and other efficiency and effectiveness metrics.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15574, pp. 437-445. Lucca, Italy, 06-10/04/2025
DOI: 10.1007/978-3-031-88714-7_43
Project(s): EFRA
Metrics:
See at:
CNR IRIS | link.springer.com | doi.org | CNR IRIS | CNR IRIS
2025
Other
Open Access
ISTI-day 2025 Proceedings
Del Corso G., Pedrotti A., Federico G., Gennaro C., Carrara F., Amato G., Di Benedetto M., Gabrielli E., Belli D., Matrullo Zoe, Miori V., Tolomei Gabriele, Waheed T., Marchetti E., Calabrò Antonello., Rossetti G., Stella Massimo, Cazabet Rémy, Abramski K., Cau E., Citraro S., Failla A., Mesina V., Morini V., Pansanella V., Colantonio S., Germanese D., Pascali M. A., Bianchi L., Messina N., Falchi F., Barsellotti L., Pacini G., Cassese M., Puccetti G., Esuli A., Volpi L., Moreo Alejandro, Sebastiani F., Sperduti G., Nguyen Dong, Broccia G., Ter Beek M. H., Ferrari A., Massink M., Belmonte Gina, Ciancia V., Papini O., Canapa G., Catricalà B., Manca M., Paternò F., Santoro C., Zedda E., Gallo S., Maenza S., Mattioli A., Simeoli L., Rucci D., Carlini E., Dazzi P., Kavalionak H., Mordacchini M., Rulli C., Muntean Cristina Ioana, Nardini F. M., Perego R., Rocchietti G., Lettich F., Renso C., Pugliese C., Casini G., Haldimann Jonas, Meyer Thomas, Assante M., Candela L., Dell'Amico A., Frosini L., Mangiacrapa F., Oliviero A., Pagano P., Panichi G., Peccerillo B., Procaccini M., Mannocci A., Manghi P., Lonetti F., Kang Dongjae, Di Giandomenico F., Jee Eunkyoung, Lazzini G., Conti F., Scopigno R., D'Acunto M., Moroni D., Cafiso M., Paradisi P., Callieri M., Pavoni G., Corsini M., De Falco A., Sala F., Saraceni Q., Gattiglia Gabriele
ISTI-Day is an annual information and networking event organized by the Institute of Information Science and Technologies "A. Faedo" (ISTI) of the Italian National Research Council (CNR). This event features an opening talk of the Director of the Dept. DIITET (Emilio F. Campana) as well as an overview of the Institute's activities presented by the ISTI Director (Roberto Scopigno). Those institutional segments are complemented by dedicated presentations and round tables featuring former staff members, as well as internal and external collaborators. To foster a network of knowledge and collaboration among newcomers, the 2025 ISTI Day edition also includes a large poster session that provides a comprehensive overview of current research activities. Each of the 13 laboratories contributes 1–3 posters, highlighting the most innovative work and offering early-career researchers a platform for discussion. Thus these proceedings include the posters selected for ISTI-Day 2025, reflecting the diverse and innovative nature of the Institute's research.
Del Corso G., Pedrotti A., Federico G., Gennaro C., Carrara F., Amato G., Di Benedetto M., Gabrielli E., Belli D., Matrullo Zoe, Miori V., Tolomei Gabriele, Waheed T., Marchetti E., Calabrò Antonello., Rossetti G., Stella Massimo, Cazabet Rémy, Abramski K., Cau E., Citraro S., Failla A., Mesina V., Morini V., Pansanella V., Colantonio S., Germanese D., Pascali M. A., Bianchi L., Messina N., Falchi F., Barsellotti L., Pacini G., Cassese M., Puccetti G., Esuli A., Volpi L., Moreo Alejandro, Sebastiani F., Sperduti G., Nguyen Dong, Broccia G., Ter Beek M. H., Ferrari A., Massink M., Belmonte Gina, Ciancia V., Papini O., Canapa G., Catricalà B., Manca M., Paternò F., Santoro C., Zedda E., Gallo S., Maenza S., Mattioli A., Simeoli L., Rucci D., Carlini E., Dazzi P., Kavalionak H., Mordacchini M., Rulli C., Muntean Cristina Ioana, Nardini F. M., Perego R., Rocchietti G., Lettich F., Renso C., Pugliese C., Casini G., Haldimann Jonas, Meyer Thomas, Assante M., Candela L., Dell'Amico A., Frosini L., Mangiacrapa F., Oliviero A., Pagano P., Panichi G., Peccerillo B., Procaccini M., Mannocci A., Manghi P., Lonetti F., Kang Dongjae, Di Giandomenico F., Jee Eunkyoung, Lazzini G., Conti F., Scopigno R., D'Acunto M., Moroni D., Cafiso M., Paradisi P., Callieri M., Pavoni G., Corsini M., De Falco A., Sala F., Saraceni Q., Gattiglia Gabriele
ISTI-Day is an annual information and networking event organized by the Institute of Information Science and Technologies "A. Faedo" (ISTI) of the Italian National Research Council (CNR). This event features an opening talk of the Director of the Dept. DIITET (Emilio F. Campana) as well as an overview of the Institute's activities presented by the ISTI Director (Roberto Scopigno). Those institutional segments are complemented by dedicated presentations and round tables featuring former staff members, as well as internal and external collaborators. To foster a network of knowledge and collaboration among newcomers, the 2025 ISTI Day edition also includes a large poster session that provides a comprehensive overview of current research activities. Each of the 13 laboratories contributes 1–3 posters, highlighting the most innovative work and offering early-career researchers a platform for discussion. Thus these proceedings include the posters selected for ISTI-Day 2025, reflecting the diverse and innovative nature of the Institute's research.
See at:
CNR IRIS | www.isti.cnr.it | CNR IRIS
2025
Conference article
Open Access
Efficient re-ranking with cross-encoders via early exit
Busolin F., Lucchese C., Nardini F. M., Orlando S., Perego R., Trani S., Veneri A.
Pre-trained language models based on transformer networks arehighly effective for document re-ranking in ad-hoc search. Amongthese, cross-encoders stand out for their effectiveness, as they pro-cess query-document pairs through the entire transformer networkto compute ranking scores. However, this traversal is computation-ally expensive. To address this, prior work has explored early-exitstrategies, enabling the model to terminate the traversal of query-document pairs. These techniques rely on learned classifiers, placedafter each transformer block, that decide if a query-document paircan be dropped. Diverging from previous approaches, we proposeSimilarity-based Early Exit ( SEE ), a novel—non-learned—strategythat exploits the similarities between query and document tokenembeddings to early-terminate the inference of documents that willmost likely be non-relevant to the query. Even though SEE can beused after every transformer block, we show that the best advan-tage is achieved when applied before the first transformer block,thus saving most of the inference cost for the query-document pairs.Reproducible experiments on 17 public datasets covering in-domainand out-of-domain evaluation show that SEE can be effectively ap-plied to four different cross-encoders, achieving speedups of up to3.5× with a limited loss in ranking effectiveness.DOI: 10.1145/3726302.3729962
Metrics:
Busolin F., Lucchese C., Nardini F. M., Orlando S., Perego R., Trani S., Veneri A.
Pre-trained language models based on transformer networks arehighly effective for document re-ranking in ad-hoc search. Amongthese, cross-encoders stand out for their effectiveness, as they pro-cess query-document pairs through the entire transformer networkto compute ranking scores. However, this traversal is computation-ally expensive. To address this, prior work has explored early-exitstrategies, enabling the model to terminate the traversal of query-document pairs. These techniques rely on learned classifiers, placedafter each transformer block, that decide if a query-document paircan be dropped. Diverging from previous approaches, we proposeSimilarity-based Early Exit ( SEE ), a novel—non-learned—strategythat exploits the similarities between query and document tokenembeddings to early-terminate the inference of documents that willmost likely be non-relevant to the query. Even though SEE can beused after every transformer block, we show that the best advan-tage is achieved when applied before the first transformer block,thus saving most of the inference cost for the query-document pairs.Reproducible experiments on 17 public datasets covering in-domainand out-of-domain evaluation show that SEE can be effectively ap-plied to four different cross-encoders, achieving speedups of up to3.5× with a limited loss in ranking effectiveness.DOI: 10.1145/3726302.3729962
Metrics:
See at:
dl.acm.org | CNR IRIS | doi.org | CNR IRIS
2025
Journal article
Open Access
Neural network compression using binarization and few full-precision weights
Nardini F. M., Rulli C., Trani S., Venturini R.
Quantization and pruning are two effective Deep Neural Network model compression methods. In this paper, we propose Automatic Prune Binarization (APB), a novel compression technique combining quantization with pruning. APB enhances the representational capability of binary networks using a few full-precision weights. Our technique jointly maximizes the accuracy of the network while minimizing its memory impact by deciding whether each weight should be binarized or kept in full precision. We show how to efficiently perform a forward pass through layers compressed using APB by decomposing it into a binary and a sparse-dense matrix multiplication. Moreover, we design two novel efficient algorithms for extremely quantized matrix multiplication on CPU, leveraging highly efficient bitwise operations. The proposed algorithms are 6.9× and 1.5× faster than available state-of-the-art solutions. We extensively evaluate APB on two widely adopted model compression datasets, namely CIFAR-10 and ImageNet. APB shows to deliver better accuracy/memory trade-off compared to state-of-the-art methods based on i) quantization, ii) pruning, and iii) a combination of pruning and quantization. APB also outperforms quantization in the accuracy/efficiency trade-off, being up to 2× faster than the 2-bits quantized model with no loss in accuracy.Source: INFORMATION SCIENCES, vol. 716
DOI: 10.1016/j.ins.2025.122251
DOI: 10.2139/ssrn.4927691
DOI: 10.48550/arxiv.2306.08960
Project(s): EFRA
Metrics:
Nardini F. M., Rulli C., Trani S., Venturini R.
Quantization and pruning are two effective Deep Neural Network model compression methods. In this paper, we propose Automatic Prune Binarization (APB), a novel compression technique combining quantization with pruning. APB enhances the representational capability of binary networks using a few full-precision weights. Our technique jointly maximizes the accuracy of the network while minimizing its memory impact by deciding whether each weight should be binarized or kept in full precision. We show how to efficiently perform a forward pass through layers compressed using APB by decomposing it into a binary and a sparse-dense matrix multiplication. Moreover, we design two novel efficient algorithms for extremely quantized matrix multiplication on CPU, leveraging highly efficient bitwise operations. The proposed algorithms are 6.9× and 1.5× faster than available state-of-the-art solutions. We extensively evaluate APB on two widely adopted model compression datasets, namely CIFAR-10 and ImageNet. APB shows to deliver better accuracy/memory trade-off compared to state-of-the-art methods based on i) quantization, ii) pruning, and iii) a combination of pruning and quantization. APB also outperforms quantization in the accuracy/efficiency trade-off, being up to 2× faster than the 2-bits quantized model with no loss in accuracy.Source: INFORMATION SCIENCES, vol. 716
DOI: 10.1016/j.ins.2025.122251
DOI: 10.2139/ssrn.4927691
DOI: 10.48550/arxiv.2306.08960
Project(s): EFRA
Metrics:
See at:
arXiv.org e-Print Archive | Information Sciences | CNR IRIS | www.sciencedirect.com | ZENODO | Software Heritage | Software Heritage | doi.org | doi.org | GitHub | GitHub | GitHub | GitHub | GitHub | CNR IRIS
2025
Conference article
Open Access
CoSRec: a joint conversational search and recommendation dataset
Alessio M., Merlo S., Di Noia T., Faggioli G., Ferrante M., Ferro N., Muntean Cristina Ioana, Nardini F. M., Narducci F., Perego R., Santucci G., Viterbo N.
Conversational Information Access systems have experienced wide-spread diffusion thanks to the natural and effortless interactionsthey enable with the user. In particular, they represent an effectiveinteraction interface for conversational search (CS) and conversa-tional recommendation (CR) scenarios. Despite their commonali-ties, CR and CS systems are often devised, developed, and evalu-ated as isolated components. Integrating these two elements wouldallow for handling complex information access scenarios, suchas exploring unfamiliar recommended product aspects, enablingricher dialogues, and improving user satisfaction. As of today, thescarce availability of integrated datasets — focused exclusively oneither of the tasks — limits the possibilities for evaluating by-designintegrated CS and CR systems. To address this gap, we proposeCoSRec1, the first dataset for joint Conversational Search and Rec-ommendation (CSR) evaluation. The CoSRec test set includes 20high-quality conversations, with human-made annotations for thequality of conversations, and manually crafted relevance judgmentsfor products and documents. Additionally, we provide supplemen-tary training data comprising partially annotated dialogues and rawconversations to support diverse learning paradigms. CoSRec is the first resource to model CR and CS tasks in a unified framework,enabling the training and evaluation of systems that must shiftbetween answering queries and making suggestions dynamically.DOI: 10.1145/3726302.3730319
Metrics:
Alessio M., Merlo S., Di Noia T., Faggioli G., Ferrante M., Ferro N., Muntean Cristina Ioana, Nardini F. M., Narducci F., Perego R., Santucci G., Viterbo N.
Conversational Information Access systems have experienced wide-spread diffusion thanks to the natural and effortless interactionsthey enable with the user. In particular, they represent an effectiveinteraction interface for conversational search (CS) and conversa-tional recommendation (CR) scenarios. Despite their commonali-ties, CR and CS systems are often devised, developed, and evalu-ated as isolated components. Integrating these two elements wouldallow for handling complex information access scenarios, suchas exploring unfamiliar recommended product aspects, enablingricher dialogues, and improving user satisfaction. As of today, thescarce availability of integrated datasets — focused exclusively oneither of the tasks — limits the possibilities for evaluating by-designintegrated CS and CR systems. To address this gap, we proposeCoSRec1, the first dataset for joint Conversational Search and Rec-ommendation (CSR) evaluation. The CoSRec test set includes 20high-quality conversations, with human-made annotations for thequality of conversations, and manually crafted relevance judgmentsfor products and documents. Additionally, we provide supplemen-tary training data comprising partially annotated dialogues and rawconversations to support diverse learning paradigms. CoSRec is the first resource to model CR and CS tasks in a unified framework,enabling the training and evaluation of systems that must shiftbetween answering queries and making suggestions dynamically.DOI: 10.1145/3726302.3730319
Metrics:
See at:
dl.acm.org | CNR IRIS | Padua research Archive (Archivio istituzionale della ricerca - Università di Padova) | Padua research Archive (Archivio istituzionale della ricerca - Università di Padova) | CNR IRIS
2025
Book
Open Access
Early-exit graph neural networks
Di Francesco A. G., Bucarelli M. S., Nardini F. M., Perego R., Tonellotto N., Silvestri F.
Early-exit mechanisms allow deep neural networks to stop inference once prediction confidence is high, reducing latency and energy on easy inputs while retaining full-depth accuracy on harder ones. Similarly, adding early exit mechanisms to Graph Neural Networks (GNNs), the go-to models for graph-structured data, allows for dynamic trading depth for confidence on simple graphs while maintaining full-depth accuracy on harder ones to capture intricate relationships. Yet, their potential in deep GNNs, where over-smoothing, over-squashing or more generally vanishing gradients prevent these model to properly learn, remains largely unexplored. To address this, we introduce Symmetric-Anti-Symmetric GNNs (SAS-GNN), whose symmetry-based inductive biases yield stable intermediate representations that support safe early exits. Building on this backbone, we propose Early-Exit GNNs (EEGNNs), which attach confidence-aware exit neural heads which are trainable end-to-end based on the task objective, enabling on-the-fly termination at node or graph level. Experiments show that EEGNNs learn task-driven exit strategies, while achieving competitive results on heterophilic graphs and long-range tasks. Even when not outperforming the strongest baselines, EEGNNs consistently deliver favorable accuracy-efficiency trade-offs thanks to their adaptive and parameter-efficient design. We plan to release the code to reproduce our experiments.DOI: 10.48550/arxiv.2505.18088
Metrics:
Di Francesco A. G., Bucarelli M. S., Nardini F. M., Perego R., Tonellotto N., Silvestri F.
Early-exit mechanisms allow deep neural networks to stop inference once prediction confidence is high, reducing latency and energy on easy inputs while retaining full-depth accuracy on harder ones. Similarly, adding early exit mechanisms to Graph Neural Networks (GNNs), the go-to models for graph-structured data, allows for dynamic trading depth for confidence on simple graphs while maintaining full-depth accuracy on harder ones to capture intricate relationships. Yet, their potential in deep GNNs, where over-smoothing, over-squashing or more generally vanishing gradients prevent these model to properly learn, remains largely unexplored. To address this, we introduce Symmetric-Anti-Symmetric GNNs (SAS-GNN), whose symmetry-based inductive biases yield stable intermediate representations that support safe early exits. Building on this backbone, we propose Early-Exit GNNs (EEGNNs), which attach confidence-aware exit neural heads which are trainable end-to-end based on the task objective, enabling on-the-fly termination at node or graph level. Experiments show that EEGNNs learn task-driven exit strategies, while achieving competitive results on heterophilic graphs and long-range tasks. Even when not outperforming the strongest baselines, EEGNNs consistently deliver favorable accuracy-efficiency trade-offs thanks to their adaptive and parameter-efficient design. We plan to release the code to reproduce our experiments.DOI: 10.48550/arxiv.2505.18088
Metrics:
See at:
arXiv.org e-Print Archive | CNR IRIS | doi.org | CNR IRIS
2025
Conference article
Open Access
Efficient conversational search via topical locality in dense retrieval
Muntean Cristina Ioana, Nardini F. M., Perego R., Rocchietti G., Rulli C.
Pre-trained language models have been widely exploited to learn dense representations of documents and queries for information retrieval. While previous efforts have primarily focused on improving effectiveness and user satisfaction, response time remains a critical bottleneck of conversational search systems. To address this, we exploit the topical locality inherent in conversational queries, i.e., the tendency of queries within a conversation to focus on related topics. By leveraging query embedding similarities, we dynamically restrict the search space to semantically relevant document clusters, reducing computational complexity without compromising retrieval quality. We evaluate our approach on the TREC CAsT, 2019 and 2020 datasets using multiple embedding models and vector indexes, achieving improvements in processing speed of up to 10.3X with little loss in performance (4.3X without any loss). Our results show that the proposed system effectively handles complex, multi-turn queries with high precision and efficiency, offering a practical solution for real-time conversational search.DOI: 10.1145/3726302.3730186
DOI: 10.48550/arxiv.2504.21507
Project(s): EFRA, Future Artificial Intelligence Research” - Spoke 1” Human-centered AI”
Metrics:
Muntean Cristina Ioana, Nardini F. M., Perego R., Rocchietti G., Rulli C.
Pre-trained language models have been widely exploited to learn dense representations of documents and queries for information retrieval. While previous efforts have primarily focused on improving effectiveness and user satisfaction, response time remains a critical bottleneck of conversational search systems. To address this, we exploit the topical locality inherent in conversational queries, i.e., the tendency of queries within a conversation to focus on related topics. By leveraging query embedding similarities, we dynamically restrict the search space to semantically relevant document clusters, reducing computational complexity without compromising retrieval quality. We evaluate our approach on the TREC CAsT, 2019 and 2020 datasets using multiple embedding models and vector indexes, achieving improvements in processing speed of up to 10.3X with little loss in performance (4.3X without any loss). Our results show that the proposed system effectively handles complex, multi-turn queries with high precision and efficiency, offering a practical solution for real-time conversational search.DOI: 10.1145/3726302.3730186
DOI: 10.48550/arxiv.2504.21507
Project(s): EFRA
, Future Artificial Intelligence Research” - Spoke 1” Human-centered AI”Metrics:
See at:
arXiv.org e-Print Archive | dl.acm.org | CNR IRIS | doi.org | doi.org | Archivio della Ricerca - Università di Pisa | CNR IRIS
2025
Conference article
Open Access
Power- and fragmentation-aware online scheduling for GPU datacenters
Lettich F., Carlini E., Nardini F. M., Perego R., Trani S.
The rise of Artificial Intelligence and Large Language Models is driving increased GPU usage in data centers for complex training and inference tasks, impacting operational costs, energy demands, and the environmental footprint of large-scale computing infrastructures. This work addresses the online scheduling problem in GPU datacenters, which involves scheduling tasks without knowledge of their future arrivals. We focus on two objectives: minimizing GPU fragmentation and reducing power consumption. GPU fragmentation occurs when partial GPU allocations hinder the efficient use of remaining resources, especially as the datacenter nears full capacity. A recent scheduling policy, Fragmentation Gradient Descent (FGD), leverages a fragmentation metric to address this issue. Reducing power consumption is also crucial due to the significant power demands of GPUs. To this end, we propose PWR, a novel scheduling policy to minimize power usage by selecting power-efficient GPU and CPU combinations. This involves a simplified model for measuring power consumption integrated into a Kubernetes score plugin. Through an extensive experimental evaluation in a simulated cluster, we show how PWR, when combined with FGD, achieves a balanced trade-off between reducing power consumption and minimizing GPU fragmentation.DOI: 10.1109/ccgrid64434.2025.00015
Project(s): EFRA, Spoke 1 ”Human-centered AI” of the M4C2 - Investimento 1.3, Partenariato Esteso PE00000013 - ”FAIR - Future Artificial Intelligence Research”
Metrics:
Lettich F., Carlini E., Nardini F. M., Perego R., Trani S.
The rise of Artificial Intelligence and Large Language Models is driving increased GPU usage in data centers for complex training and inference tasks, impacting operational costs, energy demands, and the environmental footprint of large-scale computing infrastructures. This work addresses the online scheduling problem in GPU datacenters, which involves scheduling tasks without knowledge of their future arrivals. We focus on two objectives: minimizing GPU fragmentation and reducing power consumption. GPU fragmentation occurs when partial GPU allocations hinder the efficient use of remaining resources, especially as the datacenter nears full capacity. A recent scheduling policy, Fragmentation Gradient Descent (FGD), leverages a fragmentation metric to address this issue. Reducing power consumption is also crucial due to the significant power demands of GPUs. To this end, we propose PWR, a novel scheduling policy to minimize power usage by selecting power-efficient GPU and CPU combinations. This involves a simplified model for measuring power consumption integrated into a Kubernetes score plugin. Through an extensive experimental evaluation in a simulated cluster, we show how PWR, when combined with FGD, achieves a balanced trade-off between reducing power consumption and minimizing GPU fragmentation.DOI: 10.1109/ccgrid64434.2025.00015
Project(s): EFRA
, Spoke 1 ”Human-centered AI” of the M4C2 - Investimento 1.3, Partenariato Esteso PE00000013 - ”FAIR - Future Artificial Intelligence Research”Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | CNR IRIS | CNR IRIS
2025
Conference article
Open Access
FoodSafeSum: enabling natural language processing applications for food safety document summarization and analysis
Bakagianni J., Randl K., Rocchietti G., Rulli C., Nardini F. M., Henriksson A., Trani S., Romanova A., Pavlopoulos J.
Food safety demands timely detection, regulation, and public communication, yet the lack of structured datasets hinders Natural Language Processing (NLP) research. We present and release a new dataset of human-written and Large Language Model (LLM)-generated summaries of food safety documents, plus food safety related metadata. We evaluate its utility on three NLP tasks directly reflecting food safety practices: multilabel classification for organizing documents into domain-specific categories; document retrieval for accessing regulatory and scientific evidence; and question answering via retrieval-augmented generation that improves factual accuracy. We show that LLM summaries perform comparably or better than human ones across tasks. We also demonstrate clustering of summaries for event tracking and compliance monitoring. This dataset enables NLP applications that support core food safety practices, including the organization of regulatory and scientific evidence, monitoring of compliance issues, and communication of risks to the public.DOI: 10.18653/v1/2025.findings-emnlp.911
Project(s): MIS
Metrics:
Bakagianni J., Randl K., Rocchietti G., Rulli C., Nardini F. M., Henriksson A., Trani S., Romanova A., Pavlopoulos J.
Food safety demands timely detection, regulation, and public communication, yet the lack of structured datasets hinders Natural Language Processing (NLP) research. We present and release a new dataset of human-written and Large Language Model (LLM)-generated summaries of food safety documents, plus food safety related metadata. We evaluate its utility on three NLP tasks directly reflecting food safety practices: multilabel classification for organizing documents into domain-specific categories; document retrieval for accessing regulatory and scientific evidence; and question answering via retrieval-augmented generation that improves factual accuracy. We show that LLM summaries perform comparably or better than human ones across tasks. We also demonstrate clustering of summaries for event tracking and compliance monitoring. This dataset enables NLP applications that support core food safety practices, including the organization of regulatory and scientific evidence, monitoring of compliance issues, and communication of risks to the public.DOI: 10.18653/v1/2025.findings-emnlp.911
Project(s): MIS
Metrics:
See at:
aclanthology.org | CNR IRIS | doi.org | CNR IRIS
2024
Journal article
Open Access
Report on the 13th Italian Information Retrieval Workshop (IIR 2023)
Faggioli G., Ferrara A., Nardini F. M., Tonellotto N.
The 13th Italian Information Retrieval Workshop is the thirteenth edition of the annual conference of the Italian information retrieval and recommender systems communities. The two days of the conference gathered interesting studies and research work on a wide range of topics on information retrieval, recommender systems, and natural language processing, such as Search and Ranking, Recommendation, Content Analysis, and Classification, Artificial Intelligence, NLP, Semantics, and Dialog, Domain-Specific Applications, Human Factors and Interfaces, and Evaluation. It was hosted by the National Research Council (CNR) of Italy and the University of Pisa in a conference facility in Pisa, Italy.Source: SIGIR FORUM, vol. 57 (issue 2), pp. 1-12
DOI: 10.1145/3642979.3642990
Metrics:
Faggioli G., Ferrara A., Nardini F. M., Tonellotto N.
The 13th Italian Information Retrieval Workshop is the thirteenth edition of the annual conference of the Italian information retrieval and recommender systems communities. The two days of the conference gathered interesting studies and research work on a wide range of topics on information retrieval, recommender systems, and natural language processing, such as Search and Ranking, Recommendation, Content Analysis, and Classification, Artificial Intelligence, NLP, Semantics, and Dialog, Domain-Specific Applications, Human Factors and Interfaces, and Evaluation. It was hosted by the National Research Council (CNR) of Italy and the University of Pisa in a conference facility in Pisa, Italy.Source: SIGIR FORUM, vol. 57 (issue 2), pp. 1-12
DOI: 10.1145/3642979.3642990
Metrics:
See at:
dl.acm.org | CNR IRIS | ACM SIGIR Forum | CNR IRIS