CSIRO - LT at S em E val-2025 Task 11: Adapting LLM s for Emotion Recognition for Multiple Languages (2025)

Abstract: Detecting emotions across different languages is challenging due to the varied and culturally nuanced ways of emotional expressions. The Semeval 2025 Task 11: Bridging the Gap in Text-Based emotion shared task was organised to investigate emotion recognition across different languages. The goal of the task is to implement an emotion recogniser that can identify the basic emotional states that general third-party observers would attribute to an author based on their written text snippet, along with the intensity of those emotions. We report our investigation of various task-adaptation strategies for LLMs in emotion recognition. We show that the most effective method for this task is to fine-tune a pre-trained multilingual LLM for each language.

## introduction text-based emotion recognition plays a crucial role in studies related to mental health @xcite , emotional intelligence @xcite , and human-computer interaction @xcite . however, recognising emotions in text remains a significant challenge, especially across different languages @xcite . linguistic variations, cultural differences in emotional expression, and the scarcity of annotated data for low-resourced languages make emotion recognition particularly complex @xcite @xcite . the semeval-2025 task 11: bridging the gap in text-based emotion shared task @xcite 1 is part of the international workshop on semantic evaluation (semeval). its objective is to detect collectively perceived emotions within text 1 https://github.com/emotion-analysis-project/ semeval2025-task11 snippets written in 32 different languages. collectively perceived emotion refers to the basic emotional states-such as anger, joy, and disgust-that third-party observers from the general public can attribute to a text snippet generated by a writer. unlike tasks aimed at identifying the writer's actual emotional states or the emotional states evoked in individual reader @xcite , this task emphasises the shared perception of emotions by general readers. this distinction is crucial, as perceived emotions can vary significantly from both intended and personally experienced emotions. the shared task consists of three tracks: (a) multi-label emotion detection, where the goal is to predict the perceived emotional states expressed in a given text snippet, including joy, sadness, fear, anger, surprise, and disgust; (b) emotion intensity, where the objective is to predict the intensity scale of each perceived emotional state for a given text snippet. each emotional state is rated on a 4-point categorical scale: 0 (no emotion), 1 (low intensity), 2 (moderate intensity), and 3 (high intensity); and (c) cross-lingual emotion detection, where the goal is to predict the perceived emotion on text snippets written in a language different from the one used for model training, such as training on english-written data but making emotion recognition on javanese-written data. our team participated in tracks a and b, focusing on fine-tuning large language models (llms). llms have contributed to significant improvements across different nlp tasks @xcite . however, previous studies suggest that they are ineffective for emotion classification in zero-shot and few-shot settings, even when provided with in-context learning (icl) prompts @xcite . a potential improvement can be achieved by fine-tuning llms with instructions @xcite . drawing inspiration from @xcite , we ex-plore instruction-tuning and continual fine-tuning of multilingual llms. we then compare the effectiveness of this adaptation for emotion recognition across different languages by fine-tuning a multilingual model individually for each language. we also propose several adaptation approaches and conduct a comparative analysis across these approaches, specifically for track a. we develop an adaptation strategy for llms in emotion recognition that is effective across languages of varying resourced levels (see the categorisation of resource abundance by @xcite al. (2020); üstün et al. (2024) ). ## conclusions we participated in the semeval 2025 task 11: bridging the gap in text-based emotion shared task, which included tracks for multi-label emotion detection (track a) and emotion intensity detection (track b). for track a, we approached it as a binary classification problem for each emotiondetermining whether an emotional state is present in or absent from a given input text snippet. we found that direct supervised fine-tuning can effectively adapt llms for the detection of emotions for most languages, except for the lower-resourced languages where few-shot learning is more effective. for track b, we achieved the best results by employing different llms (both direct sft and instruction-tuned) for each language. the results in both tracks suggest that, when adapting llms for emotion recognition on most mid-resourced and higher-resourced language, instruction-tuning was not as effective as in other nlp tasks. a more suitable approach is to directly apply supervised fine-tuning of llms on task-specific datasets.