Abstract
Pre-trained language models (PLMs) perform well in In-Topic setups, where training and testing data come from the same topics. However, they face challenges in Cross-Topic scenarios where testing data is derived from distinct topics. This paper analyzes various PLMs with three probing-based experiments to better understand the reasons behind such generalization gaps. For the first time, we demonstrate that the extent of these generalization gaps and the sensitivity to token-level interventions vary significantly across PLMs. By evaluating large language models (LLMs), we show the usefulness of our analysis for these recent models. Overall, we observe diverse pre-training objectives and architectural regularization contribute to more robust PLMs and mitigate generalization gaps. Our research contributes to a deeper understanding and comparison of language models across different generalization scenarios.- Anthology ID:
- 2024.findings-eacl.146
- Volume:
- Findings of the Association for Computational Linguistics: EACL 2024
- Month:
- March
- Year:
- 2024
- Address:
- St. Julian’s, Malta
- Editors:
- Yvette Graham, Matthew Purver
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2197–2214
- Language:
- URL:
- https://aclanthology.org/2024.findings-eacl.146
- DOI:
- Cite (ACL):
- Andreas Waldis, Yufang Hou, and Iryna Gurevych. 2024. Dive into the Chasm: Probing the Gap between In- and Cross-Topic Generalization. In Findings of the Association for Computational Linguistics: EACL 2024, pages 2197–2214, St. Julian’s, Malta. Association for Computational Linguistics.
- Cite (Informal):
- Dive into the Chasm: Probing the Gap between In- and Cross-Topic Generalization (Waldis et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2024.findings-eacl.146.pdf