Large Language Models (LLMs) have rapidly become central to NLP, demonstrating their ability to adapt to various tasks through prompting techniques, including sentiment analysis. However, we still have a limited understanding of how these models capture sentiment-related information. This study probes the hidden layers of Llama models to pinpoint where sentiment features are most represented and to assess how this affects sentiment analysis.
View on arXiv@article{palma2025_2505.16491,
title={ LLaMAs Have Feelings Too: Unveiling Sentiment and Emotion Representations in LLaMA Models Through Probing },
author={ Dario Di Palma and Alessandro De Bellis and Giovanni Servedio and Vito Walter Anelli and Fedelucio Narducci and Tommaso Di Noia },
journal={arXiv preprint arXiv:2505.16491},
year={ 2025 }
}