Papers by Alberto Purpura
GRAID: Synthetic Data Generation with Geometric Constraints and Multi-Agentic Reflection for Harmful Content Detection (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are expensive to run within a large-scale system and not ideal for low-latency use cases. |
| Approach: | They propose a pipeline that leverages Large Language Models (LLMs) for dataset augmentation. |
| Outcome: | The proposed pipeline improves the performance of a harmful text classification dataset using Large Language Models (LLMs). |
Accelerating the Discovery of Semantic Associations from Medical Literature: Mining Relations Between Diseases and Symptoms (2022.emnlp-industry)
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| Challenge: | Existing methods to extract semantic associations from medical literature do not take into account the semantics of sentences from which entity co-occurrences are extracted. |
| Approach: | They propose a system for the automatic discovery of semantic associations between different entities such as diseases and their symptoms using a semantic network and a binary relation classification model trained with distant supervision. |
| Outcome: | The proposed system validates the extracted associations against a publicly available list of disease-symptom pairs against 14M PubMed abstracts. |
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. |
Deconstructing Instruction-Following: A New Benchmark for Granular Evaluation of Large Language Model Instruction Compliance Abilities (2026.eacl-long)
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| Challenge: | Existing benchmarks for ensuring Large Language Models (LLMs) follow complex instructions fail to reflect real-world use or isolate compliance from task success. |
| Approach: | They propose a modular framework that uses a dynamically generated dataset with up to 20 application-oriented generation constraints to enable a granular and independent analysis of LLM instruction compliance. |
| Outcome: | The proposed framework reveals that compliance is not a monolithic capability but varies significantly with constraint type, quantity, and position. |
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. |