Lazarus NLP at S em E val-2025 Task 11: Fine-Tuning Large Language Models for Multi-Label Emotion Classification via Sentence-Label Pairing (2025)

Abstract: Multi-label emotion classification in low-resource languages remains challenging due to limited annotated data and model adaptability. To address this, we fine-tune large language models (LLMs) using a sentence-label pairing approach, optimizing efficiency while improving classification performance. Evaluating on Sundanese, Indonesian, and Javanese, our method outperforms conventional classifier-based fine-tuning and achieves strong zero-shot cross-lingual transfer. Notably, our approach ranks first in the Sundanese subset of SemEval-2025 Task 11 Track A. Our findings demonstrate the effectiveness of LLM fine-tuning for low-resource emotion classification, underscoring the importance of tailoring adaptation strategies to specific language families in multilingual contexts.

## introduction emotion recognition is a challenging and multifaceted task that plays a crucial role in natural language processing (nlp) applications, including consumer sentiment analysis @xcite , healthcare @xcite , and humancomputer interaction @xcite . despite advancements in large language models (llms), accurately classifying nuanced emotional expressions across diverse languages remains a significant challenge, particularly for low-resource languages. @xcite addresses this challenge by providing a multilingual dataset brighter @xcite for three distinct sub-tasks: (a) multi-label emotion detection, (b) emotion intensity prediction and (c) cross-lingual emotion detection. this task aims to evaluate the ability of models to identify perceived emotions in text across 28 languages, including several underrepresented ones. in this paper, we tackled track a (supervised multi-label emotion detection) and c (cross-lingual emotion detection). our approach focuses on fine-tuning large language models using a novel sentence-label pairing strategy to enhance performance across both monolingual and cross-lingual tasks. we specifically target three languages spoken in indonesia: indonesian (ind), javanese (jav), and sundanese (sun). track a involves assigning binary labels (0 or 1) to perceived emotions, while track c evaluates a model's ability to transfer knowledge from one language to another without access to in-language training data. our methodology highlights three key contributions. first, we reformulate the multi-label classification task as a series of binary classification problems, pairing each sentence with its corresponding emotion labels. this simplifies the task into multiple single-label classifications and increases the number of training samples. second, we leverage multilingual pre-trained llms to transfer crosslingual knowledge from sundanese (supervised training set available in track a) to indonesian and javanese (test sets available in track c), taking advantage of their linguistic similarities within the same language family. finally, instead of using a conventional binary cross-entropy (bce) loss with a linear classifier head, we frame the task as text generation, training the model to output "yes" or "no" as predicted labels for each emotion. our experiments reveal the effectiveness of these strategies in improving model performance across both monolingual and cross-lingual scenarios, providing insights into addressing linguistic gaps in emotion detection tasks. traditionally, multi-label emotion classification has been approached as a conventional classification problem, where a pre-trained encoder-based language model is augmented with a linear classifier and trained using bce loss. more recent innovations have explored alternative formulations. spanemo @xcite introduced span prediction, where models learn direct associations between text spans and emotion labels, reducing ambiguity and improving classification performance on semeval benchmarks. meanwhile, t5 @xcite reframed downstream tasks (e.g. classification) within a text-to-text framework, which was later extended and scaled up by flan @xcite to instruction tuning, improving zero-shot generalization. ## multi-label emotion classification muhammad et al. (2025a) introduced brighter, an emotion recognition dataset covering 28 languages, including several low-resource ones. in this study, we focused on languages spoken in indonesia: indonesian, javanese, and sundanese, and participated in tracks that included these languages. briefly, track a is a supervised multi-label emotion detection task, where given a text snippet, the goal is to predict the speaker's perceived emotions by assigning a binary label to each (0 or 1). track b focuses on emotion intensity prediction, requiring models to predict the intensity of a given emotion on a four-level ordinal scale. however, since none of the indonesian languages were included in track b, we did not participate. track c is a cross-lingual emotion detection task, where models must predict emotions in a target language without access to a corresponding training set, relying instead on labeled data from another language. we present sample instances from the dataset in appendix a. since track a includes sundanese, we trained on its subset and applied cross-lingual transfer to indonesian and javanese for track c. this approach is feasible because all three languages share the same six emotion labels (anger, disgust, fear, joy, sadness, and surprise), eliminating the need to handle unseen emotions separately. details of each language's subset are provided in appendix a. moreover, these three languages share a common origin within the malayo-polynesian language family. previous studies @xcite suggest strong cross-lingual transfer among them, which leads us to hypothesize that our approach will be effective-especially if the pre-trained language model has been exposed to these languages during pre-training. ## system overview based on our findings and problem formulation, we propose the methodology illustrated in fig. 1 . the subsequent paragraphs provide a detailed explanation of our pipeline, including data preparation, training, and inference. ## conclusion in this study, we introduced a novel approach for multi-label emotion classification by fine-tuning llms using a sentence-label pairing method, focusing on low-resource languages of indonesia. our approach employed parameter-and memoryefficient techniques to optimize computational efficiency while preserving effectiveness. we trained llms to classify emotions from text, with sundanese as the supervised source language and also performed cross-lingual transfer to indonesian and javanese. our results demonstrated that our method outperformed conventional fine-tuning approaches in the supervised fine-tuning subtask. additionally, our method exhibited strong performance in zero-shot, cross-lingual transfer, yielding notable results on the indonesian and javanese test sets. these findings indicate that our approach is a viable solution for multi-label emotion classification, text labels predictions hater terbesar mahasiswa, dosen, selalu menolak hasil karya kita, padahal kita ya belajar, ampun2, wkwkw anger, fear, sadness, surprise joy en: the biggest haters of students are lecturers -they always reject our work, even though we're just trying to learn. have mercy, seriously, hahaha. pliss mas denn kasih tau ke istrimu untuk menutup koment ignya biar ga ada yang nyampah. sampe kapan coba komentnya diaktifkan mulu, ga tega tau. dihujat terus & dibandingbandjngkan sama orang lain. anger, disgust, fear, surprise anger, sadness en: please, mas denn, tell your wife to turn off the comments on instagram so there's no more trashtalking. how long are you going to keep the comments open? it's really sad. she keeps getting bashed and compared to others. ah gak juga gue cewek dan punya 100 daftar hal yang gue benci dari cowok wkwk anger, disgust joy, surprise en: ah, not necessarily -i'm a girl and i have a list of 100 things i hate about guys, hahaha. kata kata "cok" dikhusukan orang akrab broo, kalo belum akrab jangan kek gitu bahaya soalnya. anger, disgust, surprise disgust, fear, joy en: the word "cok" is reserved for close friends, bro. if you're not close, don't use it like that -it can be dangerous. gendre lagunya gak cocok buat salsa bintan, jadi gak greget dan seru lagi liatnya salsa bintan, anger, disgust, sadness, surprise n/a en: the music genre doesn't suit salsa bintan, so it's not exciting or fun to watch salsa bintan anymore.