The following paper presents the outcomes of a collaborative experiment on human evaluation from the ReproNLP 2024 shared task, track B, part of the ReproHum project. For this paper, we evaluated a QAG (question-answer generation) system centered on English children’s storybooks that was presented in a previous research, by using human evaluators for the study. The system generated relevant QA (Question-Answer) pairs based on a dataset with storybooks for early education (kindergarten up to middle school) called FairytaleQA. In the framework of the ReproHum project, we first outline the previous paper and the reproduction strategy that has been decided upon. The complete setup of the first human evaluation is then described, along with the modifications required to replicate it. We also add other relevant related works on this subject. In conclusion, we juxtapose the replication outcomes with those documented in the cited publication. Additionally, we explore the general features of this endeavor as well as its shortcomings.

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.