Papers by Giuseppe Tagliavini
Can Large Language Models Win the International Mathematical Games? (2025.emnlp-main)
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Alessio Cocchieri, Luca Ragazzi, Giuseppe Tagliavini, Lorenzo Tordi, Antonella Carbonaro, Gianluca Moro
| Challenge: | Recent advances in large language models (LLMs) have demonstrated strong mathematical reasoning abilities, even in visual contexts. |
| Approach: | They propose a benchmark of 2,183 high-quality mathematical problems in an open-ended format that enables a structured evaluation of LLMs’ mathematical and logical reasoning abilities. |
| Outcome: | The new benchmark spans seven age groups and a skill-based taxonomy and enables a structured evaluation of LLMs’ mathematical and logical reasoning abilities. |
OpenBioNER: Lightweight Open-Domain Biomedical Named Entity Recognition Through Entity Type Description (2025.findings-naacl)
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Alessio Cocchieri, Giacomo Frisoni, Marcos Martínez Galindo, Gianluca Moro, Giuseppe Tagliavini, Francesco Candoli
| Challenge: | Biomedical Named Entity Recognition (BioNER) is a computationally expensive and limited tool . specialized 7B NER LLMs and GPT-4o can't match textual spans with entity types . |
| Approach: | They propose a lightweight BERT-based cross-encoder architecture that can identify any biomedical entity using only its description. |
| Outcome: | The proposed system outperforms existing models that match textual spans with entity types rather than descriptions on biomedical benchmarks. |
ZeroNER: Fueling Zero-Shot Named Entity Recognition via Entity Type Descriptions (2025.findings-acl)
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Alessio Cocchieri, Marcos Martínez Galindo, Giacomo Frisoni, Gianluca Moro, Claudio Sartori, Giuseppe Tagliavini
| Challenge: | Existing zero-shot learning methods rely on entity type names for generalization . current solutions require large datasets and prioritize a handful of commonly occurring types . |
| Approach: | They propose a description-driven framework that enhances hard zero-shot NER in low-resource settings. |
| Outcome: | The proposed framework outperforms existing models by up to 16% in the F1 score . it also surpasses baseline models that use type names alone . |
LLMs (Almost) Never Abstain Under Medical Uncertainty (2026.acl-long)
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| Challenge: | Medical multiple-choice question answering (MCQA) benchmarks assume large language models should always commit to an answer. |
| Approach: | They propose a benchmark to evaluate medical abstention under uncertainty . they remove the gold answer and introduce an explicit "I abstain" option . results highlight abstinence as a critical but overlooked dimension of medical decision-making evaluation . |
| Outcome: | The new benchmark evaluates medical abstention under uncertainty. |
ReMedQA: Are We Done With Medical Multiple-Choice Benchmarks? (2026.eacl-long)
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| Challenge: | Multiple-choice question answering (MCQA) benchmarks show near-human accuracy . but a single accuracy score is a poor proxy for competence . |
| Approach: | They propose a medical multiple-choice question answering (MCQA) benchmark that augments three standard medical MCQA datasets with open-ended answers and systematically perturbed options. |
| Outcome: | The proposed benchmarks show that high MCQA accuracy masks low reliability . MCQ is the dominant paradigm for assessing medical knowledge in large language models . |
“What do you call a dog that is incontrovertibly true? Dogma”: Testing LLM Generalization through Humor (2025.acl-long)
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| Challenge: | Large language models (LLMs) have shown strong performance in NLP tasks like text summarization and question answering. |
| Approach: | They propose a new humor-based question-answering benchmark to assess LLMs’ reasoning through carefully crafted puns. |
| Outcome: | Experiments on pun comprehension, resolution, and generation reveal that most LLMs struggle with generalization, even on simple tasks, consistently underperforming the human baseline. |