Abstract
Pretrained language models are expected to effectively map input text to a set of vectors while preserving the inherent relationships within the text. Consequently, designing a white-box model to compute metrics that reflect the presence of specific internal relations in these vectors has become a common approach for post-hoc interpretability analysis of pretrained language models. However, achieving interpretability in white-box models and ensuring the rigor of metric computation becomes challenging when the source model lacks inherent interpretability. Therefore, in this paper, we discuss striking a balance in this trade-off and propose a novel line to constructing metrics for understanding the mechanisms of pretrained language models. We have specifically designed a family of metrics along this line of investigation, and the model used to compute these metrics is referred to as the tree topological probe. We conducted measurements on BERT-large by using these metrics. Based on the experimental results, we propose a speculation regarding the working mechanism of BERT-like pretrained language models, as well as a strategy for enhancing fine-tuning performance by leveraging the topological probe to improve specific submodules.- Anthology ID:
- 2023.findings-emnlp.894
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 13399–13412
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.894
- DOI:
- 10.18653/v1/2023.findings-emnlp.894
- Cite (ACL):
- You Li, Jinhui Yin, and Yuming Lin. 2023. Rethinking the Construction of Effective Metrics for Understanding the Mechanisms of Pretrained Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 13399–13412, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Rethinking the Construction of Effective Metrics for Understanding the Mechanisms of Pretrained Language Models (Li et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2023.findings-emnlp.894.pdf