Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding

This paper introduces EthioEmo, a multi-label emotion classification dataset for four Ethiopian languages, and evaluates various large language models (encoder-only, encoder-decoder, and decoder-only) on emotion understanding tasks in both low-resource and high-resource languages.

Large Language Models (LLMs) show promising learning and reasoning abilities. Compared to other NLP tasks, multilingual and multi-label emotion evaluation tasks are under-explored in LLMs. In this paper, we present EthioEmo, a multi-label emotion classification dataset for four Ethiopian languages, namely, Amharic (amh), Afan Oromo (orm), Somali (som), and Tigrinya (tir). We perform extensive experiments with an additional English multi-label emotion dataset from SemEval 2018 Task 1. Our evaluat

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