UniBuc at SemEval-2024 Task 2: Tailored Prompting with Solar for Clinical NLI

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.

Computation and Language (cs.CL)

arXiv:2610.00408 [cs.CL]

(or arXiv:2610.00408v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.00408 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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