Summary of Dadee: Unsupervised Domain Adaptation in Early Exit Plms, by Divya Jyoti Bajpai and Manjesh Kumar Hanawal
DAdEE: Unsupervised Domain Adaptation in Early Exit PLMs
by Divya Jyoti Bajpai, Manjesh Kumar Hanawal
First submitted to arxiv on: 6 Oct 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 This paper proposes Unsupervised Domain Adaptation in Early Exit (DADEE) framework, a novel approach to address the issue of large size and high inference latency in Pre-trained Language Models (PLMs). DADEE utilizes multi-level adaptation using knowledge distillation, achieving domain-invariant representations through GAN-based adversarial adaptation at each layer. This reduces the domain gap between the source and target domains across all layers. The attached exits not only speed up inference but also enhance domain adaptation by reducing catastrophic forgetting and mode collapse, making it suitable for real-world scenarios. Experiments demonstrate that DADEE consistently outperforms early exit methods and various domain adaptation methods under domain shift scenarios. The paper showcases the effectiveness of DADEE in tasks such as sentiment analysis, entailment classification, and natural language inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about making language models faster and better at adapting to new situations. Language models are really good at understanding and generating text, but they can be slow and not very good at changing what they do based on the situation. The authors propose a new way to make these models work better in different scenarios, called DADEE. This method helps the model learn from many different types of data and adapt quickly to new situations. The authors tested this method with three different tasks: understanding sentiment, determining entailment, and making predictions about natural language. They found that their method worked much better than other methods at adapting to new situations. |
Keywords
» Artificial intelligence » Classification » Domain adaptation » Gan » Inference » Knowledge distillation » Unsupervised
