Papers by Alessandra Pascale
Query-driven Document-level Scientific Evidence Extraction from Biomedical Studies (2025.acl-long)
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Massimiliano Pronesti, Joao H Bettencourt-Silva, Paul Flanagan, Alessandra Pascale, Oisín Redmond, Anya Belz, Yufang Hou
| Challenge: | Systematic reviews are widely regarded as the gold standard in evidence-based medicine, heavily influencing medical decisions made by doctors, health authorities, and patients. |
| Approach: | They propose a retrieval-augmented generation framework to tackle the unique challenges of evidence extraction by leveraging forest plots from Cochrane systematic reviews. |
| Outcome: | The proposed framework outperforms existing methods by up to 10.3% in the F1 score on this task. |
Comprehensiveness Metrics for Automatic Evaluation of Factual Recall in Text Generation (2026.findings-acl)
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| Challenge: | Large language models (LLMs) produce incomplete or selectively omit key information . omissions of key information or misrepresentation of conflicting evidence can cause harm . |
| Approach: | They propose a method that decomposes texts into atomic statements and uses natural language inference to identify missing facts and a Q A-based metric that extracts question-answer pairs and compares responses across sources. |
| Outcome: | The proposed evaluation metrics show they perform better than more complex metrics, but at a cost. |
HBCP Corpus: A New Resource for the Analysis of Behavioural Change Intervention Reports (2020.lrec-1)
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Francesca Bonin, Martin Gleize, Ailbhe Finnerty, Candice Moore, Charles Jochim, Emma Norris, Yufang Hou, Alison J. Wright, Debasis Ganguly, Emily Hayes, Silje Zink, Alessandra Pascale, Pol Mac Aonghusa, Susan Michie
| Challenge: | Automated extraction of the reports’ intervention content, population, settings and their results is essential in synthesising and summarising the literature. |
| Approach: | They propose to build a corpus of published behaviour change intervention evaluation reports aimed at smoking cessation and to release an annotation dataset. |
| Outcome: | The proposed corpus and annotation dataset are being made available to the community. |
FactCorrector: A Graph-Inspired Approach to Long-Form Factuality Correction of Large Language Models (2026.acl-long)
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Javier Carnerero-Cano, Massimiliano Pronesti, Radu Marinescu, Tigran T. Tchrakian, James Barry, Jasmina Gajcin, Yufang Hou, Alessandra Pascale, Elizabeth M. Daly
| Challenge: | Large language models (LLMs) often produce factually incorrect responses. |
| Approach: | They propose a new method that adapts across domains without retraining and leverages structured feedback to generate a correction. |
| Outcome: | The proposed method outperforms baseline methods on a VELI5 dataset and several popular long-form factuality datasets. |
FactReasoner: A Probabilistic Approach to Long-Form Factuality Assessment for Large Language Models (2025.findings-emnlp)
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Radu Marinescu, Debarun Bhattacharjya, Junkyu Lee, Tigran T. Tchrakian, Javier Carnerero-Cano, Yufang Hou, Elizabeth M. Daly, Alessandra Pascale
| Challenge: | Large language models often fail to ensure factual accuracy of outputs thus limiting reliability in real-world applications. |
| Approach: | They propose a neuro-symbolic based factuality assessment framework that employs probabilistic reasoning to evaluate the truthfulness of long-form generated responses. |
| Outcome: | The proposed framework outperforms state-of-the-art prompt-based methods in factual accuracy and recall. |