NYCU - NLP at S em E val-2025 Task 11: Assembling Small Language Models for Multilabel Emotion Detection and Intensity Prediction (2025)

Abstract: This study describes the design of the NYCU-NLP system for the SemEval-2025 Task 11 that focuses on multi-lingual text-based emotion analysis. We instruction-tuned three small language models: Gemma-2 (27B), Mistral-small-3 (22B), and Phi-4 (14B) and then assembled them as our main system architecture. Our NYCU-NLP system participated the English Track A for multilabel emotion detection and English Track B for emotion intensity prediction. Experimental results show our best-performing submission produced a macro-averaging F1 score of 0.8225, ranking second of 90 participating teams for Track A, and ranked second among 41 teams for Track B with a Pearson correlation coefficient of 0.8373.

## introduction emotion recognition is a well-known nlp task that focuses on identifying affective states from texts. people express their perceived feelings using commonly used language in highly variable ways even within the same culture or social groups @xcite @xcite . how to detect multiple perceived emotions and predict their emotion intensities is still a challenging research problem. semeval-2025 task 11 @xcite aims to determine what emotion most people would think the speaker may be feeling given a short text written by the speaker. this shared task consists of three tracks, including 1) track a (multilabel emotion detection): given a target text, predict the perceived emotions of the speaker by selecting whether each of the following emotions apply: joy, sadness, fear, anger, surprise or disgust. 2) track b (emotion intensity): given a target text and target perceived emotions, predict the intensity for each of the classes. the set of ordinal intensity includes: 0 (no emotion), 1 (low degree of emotion), 2 (moderate degree of emotion), and 3 (moderate degree of emotion). 3) track c (cross-lingual emotion): given a text written in one of 32 involved languages, predict the perceived emotion labels of a new text in a different target language. the dataset in this track has the same format as in track a. participating teams can choose to join in one or more languages and tracks based on their preference. this paper describes the nycu-nlp (national yang ming chiao tung university, natural language processing lab) system for the semeval-2025 task 11. given the promising results obtained by large language models (llm) for various nlp tasks, we aggregate several small language models (slm), which are essentially smaller versions of llm counterparts for this textbased emotion analysis task. we participated in english tracks a and b only. our system explored the use of instruction-tuned slms, including gemma-2 (27b) @xcite , mistralsmall-3 (22b) and phi-4 (14b) @xcite and then assembled the slms to detect multilabel emotions and predict their intensities for given textbased emotion analysis. experimental results showed our best submission achieved a macroaveraging f1-score of 0.8225, ranking second of 74 participating teams for track a, and produced a pearson correlation coefficient of 0.8373, also ranking second of 36 teams for track b. the rest of this paper is organized as follows. section 2 reviews recently related studies on emotion detection and intensity prediction. section 3 describes the nycu-nlp system for this shared task. section 4 presents results and performance comparisons. conclusions are drawn in section 5. ## related work empirical evaluations showed that transformerbased language models usually outperformed conventional neural networks for emotion intensity prediction @xcite . sentiment-enhanced roberta transformers were used to predict emotion and empathy intensities @xcite . a transformer-based fusion model was proposed to integrate semantic representations at different degrees of linguistic granularity for emotional intensity predication @xcite . recently, transformer-based large language models (llm) have been used for emotion detection. emollm @xcite is a series of instruction-following llms for affective analysis based on fine-tuning various llms with instruction data. the llm-gem @xcite system was designed to use gpt 3.5 for empathy intensity prediction. emotrigger @xcite was proposed to evaluate the ability of cpt-4, llama-2-chat-13b and alpaca-13b to identify emotion triggers and consider their importances for emotion detection. an assembly of the starling-7b and llama-3-8b was fine-tuned to prediction cross-lingual emotion intensity @xcite . small language models (slm) are smaller in scale and scope than their original large model counterparts, and typically include fewer than 70 billion parameters, as opposed to llms with up to trillions of parameters. slm are thus usually compact and efficient with less memory and computational power. given limited computation resources, we are motivated to explore systems based on slms for emotion detection and intensity prediction. ## the nycu-nlp system figure 1 shows our nycu-nlp system architecture for the semeval-2025 task 11. we instruction-tuned several slms and then assembled them by averaging the predicted results for multi-label emotion detection (track a) and emotion intensity prediction (track b). ## conclusions this study describes the nycu-nlp system for the semeval-2024 text-based emotion analysis task, including system design and performance evaluation. we instruction-fine-tuned the slms to effectively detect emotion categories and predict their emotion intensities. experimental results indicate that our best submission is an assembly of the gemma-2 (27b), mistral-small-3 (22b) and phi-4 (14b) models, achieving a macro-averaging f1 score of 0.8225 for the multilabel emotion detection track (ranking second out of seventy-four submissions) and a pearson correlation coefficient of 0.8373 for the emotion intensity prediction track (ranking second of thirty-six). this pilot study is our first exploration based on applying slms to text-based emotion analysis tasks. future work will exploit other advanced slms to further improve performance.