CSIRO-LT at SemEval-2025 Task 11: Adapting LLMs for Emotion Recognition for Multiple Languages

Detecting emotions across different languages is challenging due to the varied and culturally nuanced ways of emotional expressions. The $\mathit{\text{Semeval}}\mathit{\text{2025}}\mathit{\text{Task}}\mathit{\text{11:}}\mathit{\text{Bridging}}\mathit{\text{the}}\mathit{\text{Gap}}\mathit{\text{in}}\mathit{\text{Text-Based}}\mathit{\text{emotion}}$ 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 with LoRA setting separately for each language.