F i RC - NLP at S em E val-2025 Task 11: To Prompt or to Fine-Tune? Approaches for Multilingual Emotion Classification (2025)

Abstract: In this paper, we describe our system devel-oped for participation in SemEval-2025 Task11: Bridging the Gap in Text-Based EmotionDetection. We compare three approaches formultilingual, multi-label emotion classification:XLM-R, an ensemble of models (XLM-5), anda prompt-based approach. We evaluate the per-formance of these models across a diverse setof languages, ranging from high-resource tolow-resource languages

## introduction emotions are both familiar and enigmatic. we experience and manage them daily, yet they remain complex, nuanced, and often difficult to articulate @xcite . additionally, language is used in intricate ways to convey emotions @xcite . moreover, there is significant variability in how individuals perceive and express emotions, even within the same culture or social group. automatic emotion recognition encompasses several tasks, such as detecting a speaker's emotions, identifying the emotions conveyed in written text, and recognizing emotions evoked in a reader @xcite . semeval-2025 task 11 focuses on identifying the emotion that most people believe the speaker is experiencing based on a given sentence or short text snippet @xcite ). the first track addresses multi-label emotion detection across 30 languages @xcite . the goal is to predict one or more emotions that most people believe the speaker is experiencing, based on a sentence or short text snippet uttered by the speaker. the emotions include : joy, sadness, fear, anger, surprise, and disgust. in this paper, we explore two distinct approaches: fine-tuning-based approach (fba) and promptbased approach (pba). for fba, we frame the task as a multi-label sentence classification task and we experiment with monolingual and multilingual encoder models. we provide further details in section 3.2. for pba, we leverage a pre-trained large language model (llm) to classify emotions without fine-tuning. we experiment with few-shot prompting, where we provide positive and negative examples for each emotion label in the same language as the input text. the model then analyzes the input text based on these examples and produces emotion scores. for all pba experiments, we use ggpt-4o mini as our primary llm @xcite and further details of this approach are provided in section 3.1. ## background emotion detection has become crucial due to its psychological, social, and commercial significance @xcite . individuals express their emotions through language, body gestures, and facial expressions. automatic emotion recognition encompasses various sub-tasks, such as detecting a speaker's emotions, identifying the emotions conveyed in written text, or recognizing emotions evoked in a reader of a target text @xcite . ## system description we explore two distinct approaches: the fine-tuning-based approach (fba) and the prompt-based approach (pba). for the fba, we experiment with monolingual and multilingual encoder models by framing the task as a multi-label sentence classification problem. for the pba, we utilizes a pre-trained large language model (llm) to classify emotions without fine-tuning. we use an english prompt for all of the samples. we use fewshot prompting approach by providing positive and negative examples per emotion label in the same language as the input text. the model processes the input text using these examples and generates emotion scores (0 to 1). we use gpt-4o mini as our main llm for all of the pba experiments @xcite .