@inproceedings{micluta-campeanu-etal-2024-unibuc,
title = "{U}ni{B}uc at {S}em{E}val-2024 Task 2: Tailored Prompting with Solar for Clinical {NLI}",
author = "Micluta-Campeanu, Marius and
Creanga, Claudiu and
Bucur, Ana-maria and
Uban, Ana Sabina and
Dinu, Liviu P.",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Tayyar Madabushi, Harish and
Da San Martino, Giovanni and
Rosenthal, Sara and
Ros{\'a}, Aiala},
booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.semeval-1.88",
doi = "10.18653/v1/2024.semeval-1.88",
pages = "586--595",
abstract = "This paper describes the approach of the UniBuc team in tackling the SemEval 2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials. We used SOLAR Instruct, without any fine-tuning, while focusing on input manipulation and tailored prompting. By customizing prompts for individual CTR sections, in both zero-shot and few-shots settings, we managed to achieve a consistency score of 0.72, ranking 14th in the leaderboard. Our thorough error analysis revealed that our model has a tendency to take shortcuts and rely on simple heuristics, especially when dealing with semantic-preserving changes.",
}
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%0 Conference Proceedings
%T UniBuc at SemEval-2024 Task 2: Tailored Prompting with Solar for Clinical NLI
%A Micluta-Campeanu, Marius
%A Creanga, Claudiu
%A Bucur, Ana-maria
%A Uban, Ana Sabina
%A Dinu, Liviu P.
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Tayyar Madabushi, Harish
%Y Da San Martino, Giovanni
%Y Rosenthal, Sara
%Y Rosá, Aiala
%S Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F micluta-campeanu-etal-2024-unibuc
%X This paper describes the approach of the UniBuc team in tackling the SemEval 2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials. We used SOLAR Instruct, without any fine-tuning, while focusing on input manipulation and tailored prompting. By customizing prompts for individual CTR sections, in both zero-shot and few-shots settings, we managed to achieve a consistency score of 0.72, ranking 14th in the leaderboard. Our thorough error analysis revealed that our model has a tendency to take shortcuts and rely on simple heuristics, especially when dealing with semantic-preserving changes.
%R 10.18653/v1/2024.semeval-1.88
%U https://aclanthology.org/2024.semeval-1.88
%U https://doi.org/10.18653/v1/2024.semeval-1.88
%P 586-595
Markdown (Informal)
[UniBuc at SemEval-2024 Task 2: Tailored Prompting with Solar for Clinical NLI](https://aclanthology.org/2024.semeval-1.88) (Micluta-Campeanu et al., SemEval 2024)
ACL