A new multi-label emotion benchmark for four Ethiopian languages shows that fine-tuned encoder-only models outperform zero-shot and few-shot large language models, with large gaps between resource-rich and resource-poor languages.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CL 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding
A new multi-label emotion benchmark for four Ethiopian languages shows that fine-tuned encoder-only models outperform zero-shot and few-shot large language models, with large gaps between resource-rich and resource-poor languages.