Papers by Donato Crisostomi
Mergenetic: a Simple Evolutionary Model Merging Library (2025.acl-demo)
Copied to clipboard
| Challenge: | Recent work shows that combining model merging with evolutionary algorithms can boost performance, but there is currently no library for experimenting with different evolutionary algorithms and merging methods. |
| Approach: | They propose an open-source library for evolutionary model merging that enables easy composition of merging methods and evolutionary algorithms while incorporating lightweight fitness estimators to reduce evaluation costs. |
| Outcome: | The proposed library produces competitive results across languages and tasks using modest hardware. |
Mitigating the Burden of Redundant Datasets via Batch-Wise Unique Samples and Frequency-Aware Losses (2023.acl-industry)
Copied to clipboard
Donato Crisostomi, Andrea Caciolai, Alessandro Pedrani, Kay Rottmann, Alessandro Manzotti, Enrico Palumbo, Davide Bernardi
| Challenge: | Existing solutions to train deep learning models on redundant datasets are difficult to implement in industrial settings. |
| Approach: | They propose a method to eliminate duplicates at the batch level without altering the data distribution observed by the model. |
| Outcome: | The proposed approach reduces training times on models on redundant datasets by up to 87% and 46% on average, with a drop in model performance of 0.2% relative at worst. |
AVEN-GR: Attribute Value Extraction and Normalization using product GRaphs (2023.acl-industry)
Copied to clipboard
| Challenge: | Query Attribute Understanding (QAU) is a sub-component of QU that involves extracting named attributes from user queries. |
| Approach: | They propose a novel end-to-end approach that solves Named Entity Recognition and Entity Linking for QAU . they propose utilizing product graphs to enhance the representation of query entities . |
| Outcome: | The proposed approach solves Named Entity Recognition and Entity Linking and enables open-world reasoning for QAU. |