Summary of Generative Adapter: Contextualizing Language Models in Parameters with a Single Forward Pass, by Tong Chen et al.
Generative Adapter: Contextualizing Language Models in Parameters with A Single Forward Pass
by Tong Chen, Hao Fang, Patrick Xia, Xiaodong Liu, Benjamin Van Durme, Luke Zettlemoyer, Jianfeng Gao, Hao Cheng
First submitted to arxiv on: 8 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (stat.ML)
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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 introduces a novel method called GenerativeAdapter, which efficiently adapts large language models (LMs) to new contexts without the need for fine-tuning. This approach uses self-supervised learning to train an adapter generator that maps new tasks or domains to low-rank LM adapters, significantly reducing inference overhead. The authors evaluate their method on two pretrained LMs in three adaptation scenarios: knowledge acquisition from documents, learning from demonstrations, and personalization for users. In these scenarios, GenerativeAdapter achieves impressive results, including a 63.5% improvement in F1 score in the StreamingQA task and an average accuracy of 44.9 across 26 tasks in the MetaICL evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to make language models work better on new tasks or areas without making them learn everything from scratch. This method, called GenerativeAdapter, helps big language models fit into new situations quickly and efficiently. It’s like a translator that takes what we know about how language models work and uses it to make the model more accurate in new contexts. |
Keywords
» Artificial intelligence » F1 score » Fine tuning » Inference » Self supervised
