Summary of Streamadapter: Efficient Test Time Adaptation From Contextual Streams, by Dilxat Muhtar et al.
StreamAdapter: Efficient Test Time Adaptation from Contextual Streams
by Dilxat Muhtar, Yelong Shen, Yaming Yang, Xiaodong Liu, Yadong Lu, Jianfeng Liu, Yuefeng Zhan, Hao Sun, Weiwei Deng, Feng Sun, Xueliang Zhang, Jianfeng Gao, Weizhu Chen, Qi Zhang
First submitted to arxiv on: 14 Nov 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 presents StreamAdapter, a novel approach to adapt large language models (LLMs) in-context without requiring gradient updates. By directly updating model parameters from context at test time, StreamAdapter eliminates the need for explicit demonstrations, reducing inference costs while achieving comparable or superior adaptation capability. The method employs context mapping and weight absorption mechanisms to dynamically transform ICL demonstrations into parameter updates with minimal additional parameters. This approach allows for efficient inference with constant time complexity, regardless of demonstration count. Extensive experiments demonstrate the effectiveness of StreamAdapter on diverse tasks and model architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary StreamAdapter is a new way to help large language models learn from new tasks without needing lots of examples. Usually, these models need many “in-context” demonstrations to adapt to new tasks. But this approach updates the model’s parameters directly from the context at test time, so it doesn’t need those extra examples. This makes it faster and more efficient. The paper shows that StreamAdapter works well on different tasks and model types. |
Keywords
» Artificial intelligence » Inference
