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    <abstract>Patients’ knowledge about drugs and medications is crucial as it allows them to administer them safely. This knowledgefrequently comes from written prescriptions, patient information leaflets (PILs), or from reading drug Web pages. DIMMI(Drug InforMation Mining in Italian) is a challenge aiming at evaluating the proficiency of Large Language Models in extractingdrug-specific information from PILs. The challenge seeks to advance the understanding of effectiveness in processing complexmedical information in Italian, and to enhance drug information extraction and pharmacovigilance efforts. Participants areprovided with a dataset of 600 Italian PILs and the objective is to develop models capable of accurately answering specificquestions related to drug dosage, usage, side effects, drug-drug interactions. The challenge should be approached as aninformation extraction task through a zero-shot mode, purely based on the model pre-existing knowledge and understandingor through in-context learning (Retrieval-Augmented Generation (RAG) or few-shot mode). The answers generated by themodels will be compared against the gold standard (GS), created to establish a reliable, accurate, and a comprehensive setof answers against which participant submissions can be evaluated. For each drug and each information category, the GScontains the correct information extracted from the leaflets through a manual annotation.</abstract>
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%0 Conference Proceedings
%T DIMMI - Drug InforMation Mining in Italian: A CALAMITA Challenge
%A Manna, Raffaele
%A Di Buono, Maria Pia
%A Giordano, Luca
%Y Dell’Orletta, Felice
%Y Lenci, Alessandro
%Y Montemagni, Simonetta
%Y Sprugnoli, Rachele
%S Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)
%D 2024
%8 December
%I CEUR Workshop Proceedings
%C Pisa, Italy
%@ 979-12-210-7060-6
%F manna-etal-2024-dimmi
%X Patients’ knowledge about drugs and medications is crucial as it allows them to administer them safely. This knowledgefrequently comes from written prescriptions, patient information leaflets (PILs), or from reading drug Web pages. DIMMI(Drug InforMation Mining in Italian) is a challenge aiming at evaluating the proficiency of Large Language Models in extractingdrug-specific information from PILs. The challenge seeks to advance the understanding of effectiveness in processing complexmedical information in Italian, and to enhance drug information extraction and pharmacovigilance efforts. Participants areprovided with a dataset of 600 Italian PILs and the objective is to develop models capable of accurately answering specificquestions related to drug dosage, usage, side effects, drug-drug interactions. The challenge should be approached as aninformation extraction task through a zero-shot mode, purely based on the model pre-existing knowledge and understandingor through in-context learning (Retrieval-Augmented Generation (RAG) or few-shot mode). The answers generated by themodels will be compared against the gold standard (GS), created to establish a reliable, accurate, and a comprehensive setof answers against which participant submissions can be evaluated. For each drug and each information category, the GScontains the correct information extracted from the leaflets through a manual annotation.
%U https://aclanthology.org/2024.clicit-1.126/
%P 1144-1152

Markdown (Informal)

[DIMMI - Drug InforMation Mining in Italian: A CALAMITA Challenge](https://aclanthology.org/2024.clicit-1.126/) (Manna et al., CLiC-it 2024)

• DIMMI - Drug InforMation Mining in Italian: A CALAMITA Challenge (Manna et al., CLiC-it 2024)

ACL

• Raffaele Manna, Maria Pia Di Buono, and Luca Giordano. 2024. DIMMI - Drug InforMation Mining in Italian: A CALAMITA Challenge. In Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024), pages 1144–1152, Pisa, Italy. CEUR Workshop Proceedings.