Team U nibuc - NLP at S em E val-2025 Task 11: Few-shot text-based emotion detection (2025)

Abstract: This paper describes the approach of the Unibuc - NLP team in tackling the SemEval 2025 Workshop, Task 11: Bridging the Gap in Text-Based Emotion Detection. We mainly focused on experiments using large language models (Gemini, Qwen, DeepSeek) with either few-shot prompting or fine-tuning. Withour final system, for the multi-label emotion detection track (track A), we got an F1-macro of 0.7546 (26/96 teams) for the English subset, 0.1727 (35/36 teams) for the Portuguese (Mozambican) subset and 0.325 (1/31 teams) for the Emakhuwa subset.

## introduction task 11 from the international workshop on semantic evaluation @xcite focuses on identifying emotions that most people would think the speaker might feel given a short piece of text. with all the advances in the ai world, automatic emotion detection plays such an important role: it can lead to more empathetic and context-aware interactions between humans and chatbots / ai assistants, improving user experience and satisfaction; it can help monitoring mental health conditions and provide timely interventions such that more people seek for help before is too late; it can help business to understand the customers sentiments regarding different products or services and can improve their strategies. the best system developed for task 11 track a is based on gemini @xcite model using different techniques of few-shot prompting. we made our models publicly available in a github repository. ## system overview in this paper, we focused our research on three approaches: fine-tuning bert based models (3.1), and two techniques for llms: few-shot prompting (3.2) and fine-tuning (3.3). ## results we participated in track a and tested our proposed system on 3 languages: english, portuguese (mozambican) and emakhuwa. we managed to be over the baseline on 2 out of the 3 languages and secured the first position for one of them, as shown in