Summary of Fine-tuning Pre-trained Language Models For Robust Causal Representation Learning, by Jialin Yu et al.
Fine-Tuning Pre-trained Language Models for Robust Causal Representation Learning
by Jialin Yu, Yuxiang Zhou, Yulan He, Nevin L. Zhang, Ricardo Silva
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 Medium Difficulty summary: Fine-tuning pre-trained language models (PLMs) has been shown to be effective across various domains, but these methods often fail to generalize to out-of-domain (OOD) data due to their reliance on non-causal representations. To address this issue, we investigate how fine-tuned PLMs aid generalizability from single-domain scenarios under mild assumptions. Our proposed method uses a so-called causal front-door adjustment based on a decomposition assumption, leveraging fine-tuned representations as a source of data augmentation. Comprehensive experiments in both synthetic and real-world settings demonstrate the superior generalizability of our approach compared to existing methods. Our work sheds light on the domain generalization problem by introducing links between fine-tuning and causal mechanisms into representation learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research paper is about making computers understand language better, especially when it’s different from what they were trained on. Right now, these computer systems are good at understanding one type of language, but struggle with other types. The researchers found a way to make them more flexible by adjusting how they process information. They tested their method in different situations and showed that it works much better than previous approaches. |
Keywords
» Artificial intelligence » Data augmentation » Domain generalization » Fine tuning » Representation learning
