Summary of Prompting Encoder Models For Zero-shot Classification: a Cross-domain Study in Italian, by Serena Auriemma et al.
Prompting Encoder Models for Zero-Shot Classification: A Cross-Domain Study in Italian
by Serena Auriemma, Martina Miliani, Mauro Madeddu, Alessandro Bondielli, Lucia Passaro, Alessandro Lenci
First submitted to arxiv on: 30 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper addresses the challenge of limited annotated data for language models (LMs) in specific domains and low-resource languages. It explores the feasibility of using smaller, domain-specific encoder LMs with prompting techniques to enhance performance in these contexts. The study focuses on Italian bureaucratic and legal language, evaluating models on document classification and entity typing tasks, as well as intrinsic evaluations using Pseudo-Log-Likelihood. The results show that further pre-trained models are more adaptable for domain-specific tasks, even in a zero-shot setting. Calibration techniques and in-domain verbalizers enhance the efficacy of encoder models. These domain-specialized models have significant potential for research and industrial applications in the digital transformation era. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to solve a big problem: how to make language models work better with limited data and special languages like Italian. They test some new ideas, using smaller models that are good at specific jobs, along with special tricks to help them understand tricky words. The results show that these new models can do well even when they haven’t seen the type of text before! This could be very helpful for people who need to use language models in real-life situations where there isn’t a lot of data or expertise. |
Keywords
» Artificial intelligence » Classification » Encoder » Log likelihood » Prompting » Zero shot
