Papers by Gabriele Picco
Matching Pairs: Attributing Fine-Tuned Models to their Pre-Trained Large Language Models (2023.acl-long)
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| Challenge: | generative large language models (LLMs) are widely used but fine-tuned to improve performance on downstream applications leads to violations of model licenses, model theft, and copyright infringement. |
| Approach: | They propose to trace back the origin of a model trained to its pre-trained base model . they use different knowledge levels and attribution strategies to find out how the model was trained . |
| Outcome: | The proposed method can trace back 8 out of 10 fine tuned models with different knowledge levels and attribution strategies. |
Neural Unification for Logic Reasoning over Natural Language (2021.findings-emnlp)
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| Challenge: | Automated Theorem Proving (ATP) is a computer program that can show that conjectures are logical consequences of a set of axioms. |
| Approach: | They propose a transformer-based architecture for deriving conjectures given axioms . they propose 'neural unifier' and relative training procedure to train the model . |
| Outcome: | The proposed architectures are able to answer queries with deep queries with a relatively low training time. |
Zshot: An Open-source Framework for Zero-Shot Named Entity Recognition and Relation Extraction (2023.acl-demo)
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Gabriele Picco, Marcos Martinez Galindo, Alberto Purpura, Leopold Fuchs, Vanessa Lopez, Thanh Lam Hoang
| Challenge: | ZSL is a machine learning field that uses textual descriptions of entities or relations to perform tasks that are not seen during training. |
| Approach: | They propose a framework that allows researchers to compare state-of-the-art ZSL methods with standard benchmark datasets. |
| Outcome: | The proposed framework compares state-of-the-art methods with benchmark datasets and provides APIs for production under the standard SpaCy NLP pipeline. |
Description Boosting for Zero-Shot Entity and Relation Classification (2024.findings-acl)
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Gabriele Picco, Leopold Fuchs, Marcos Martínez Galindo, Alberto Purpura, Vanessa López, Hoang Thanh Lam
| Challenge: | Named Entity Recognition and Relation Extraction (RE) methods are expensive and require domain experts for data acquisition and labeling. |
| Approach: | They propose a strategy for generating variations of an initial description, a heuristic for ranking them and an ensemble method capable of boosting the predictions of zero-shot models. |
| Outcome: | The proposed method outperforms existing approaches and achieves new SOTA results on four different entity and relation classification datasets. |
Towards Protecting Vital Healthcare Programs by Extracting Actionable Knowledge from Policy (2021.findings-acl)
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Vanessa Lopez, Nagesh Yadav, Gabriele Picco, Inge Vejsbjerg, Eoin Carrol, Seamus Brady, Marco Luca Sbodio, Lam Thanh Hoang, Miao Wei, John Segrave
| Challenge: | In the U.S., an estimated annual amount of USD$20-30B is lost to Fraud, Waste and abuse (FWA) |
| Approach: | They propose a method for automatically extracting knowledge from healthcare policy documents into a semantically-meaningful knowledge graph of rules. |
| Outcome: | The proposed method fuses advances in dependency parsing with a policy ontology to transform the content of regulatory healthcare policy into human-friendly policy rules with human oversight. |