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

arXiv 2508.01161 ↗First posted 2 Aug 2025cs.CLcited by 0

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Abstract (arXiv)

Detecting emotions across different languages is challenging due to the varied and culturally nuanced ways of emotional expressions. The textitSemeval 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 with LoRA setting separately for each language.

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References · 10

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2024The Llama 3 Herd of Modelscited by 14,715
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  4. 2017Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inferencecited by 1,593
  5. 2020The State and Fate of Linguistic Diversity and Inclusion in the NLP Worldcited by 798
  6. 2024Aya Model: An Instruction Finetuned Open-Access Multilingual Language Modelcited by 295
  7. 2024EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective Analysiscited by 62
  8. 2022Best Practices in the Creation and Use of Emotion Lexiconscited by 27
  9. 2025BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languagescited by 27
  10. 2024Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understandingcited by 17

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