Prompting Encoder Models for Zero-Shot Classification: A Cross-Domain Study in Italian

• URL: http://arxiv.org/abs/2407.20654v1
• Date: Tue, 30 Jul 2024 08:50:16 GMT
• Title: Prompting Encoder Models for Zero-Shot Classification: A Cross-Domain Study in Italian
• Authors: Serena Auriemma, Martina Miliani, Mauro Madeddu, Alessandro Bondielli, Lucia Passaro, Alessandro Lenci,
• Abstract summary: This paper explores the feasibility of employing smaller, domain-specific encoder LMs alongside prompting techniques to enhance performance in specialized contexts. Our study concentrates on the Italian bureaucratic and legal language, experimenting with both general-purpose and further pre-trained encoder-only models. The results indicate that while further pre-trained models may show diminished robustness in general knowledge, they exhibit superior adaptability for domain-specific tasks, even in a zero-shot setting.
• Score: 75.94354349994576
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Addressing the challenge of limited annotated data in specialized fields and low-resource languages is crucial for the effective use of Language Models (LMs). While most Large Language Models (LLMs) are trained on general-purpose English corpora, there is a notable gap in models specifically tailored for Italian, particularly for technical and bureaucratic jargon. This paper explores the feasibility of employing smaller, domain-specific encoder LMs alongside prompting techniques to enhance performance in these specialized contexts. Our study concentrates on the Italian bureaucratic and legal language, experimenting with both general-purpose and further pre-trained encoder-only models. We evaluated the models on downstream tasks such as document classification and entity typing and conducted intrinsic evaluations using Pseudo-Log-Likelihood. The results indicate that while further pre-trained models may show diminished robustness in general knowledge, they exhibit superior adaptability for domain-specific tasks, even in a zero-shot setting. Furthermore, the application of calibration techniques and in-domain verbalizers significantly enhances the efficacy of encoder models. These domain-specialized models prove to be particularly advantageous in scenarios where in-domain resources or expertise are scarce. In conclusion, our findings offer new insights into the use of Italian models in specialized contexts, which may have a significant impact on both research and industrial applications in the digital transformation era.

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