用LoRA微调多语言大模型，提升跨语言情绪识别效果
CSIRO-LT at SemEval-2025 Task 11: Adapting LLMs for Emotion Recognition for Multiple Languages
- 针对每种语言单独用LoRA微调多语言大模型
- 在多个语言上均实现较高情绪识别准确率
- 适合需要跨语言情绪分析的研究与应用
跨语言情绪识别因情感表达的多样性和文化差异而极具挑战性。Semeval 2025 Task 11：弥合基于文本的情绪识别差距）共享任务旨在探索不同语言间的情绪识别方法。该任务目标是构建一个情绪识别系统，能够根据作者的文本片段识别出普通第三方观察者可能归因的基本情绪状态及其强度。本文报告了多种大模型任务适配策略的探索。结果表明，对预训练的多语言大模型分别使用LoRA设置进行各语言独立微调，是本任务中最有效的方法。
原文摘要 · Abstract (English)
Detecting emotions across different languages is challenging due to the varied and culturally nuanced ways of emotional expressions. The \textit{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 with LoRA setting separately for each language.
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