cs.CLSep 30, 2026

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

Authors: Marius Micluta-Campeanu, Claudiu Creanga, Ana-Maria Bucur, Ana Sabina Uban, Liviu P. Dinu

Organizations: Interdisciplinary School of Doctoral Studies, ♡HLT Research Center University of Bucharest, Romania · Faculty of Mathematics and Computer Science

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.

Figures & tables

Appendix figures & tables4 assets

Supplementary material from the paper’s appendix.

Appendix

Explore similar work

CardsList
  1. Faithful by Design: Evaluating and Improving LLM-Generated Clinical Trial Summaries for Multi-Stakeholder Audiences

    Jul 10, 2026Robert WilliamsClinical TrialsSummarization

  2. RETUYT-INCO at BEA 2026 Shared Task 2: Meta-prompting in Rubric-based Scoring for German

    May 11, 2026Ignacio Sastre, Ignacio Remersaro, Facundo Díaz +4Rubric-Based Scoring