Summary of Switchable Decision: Dynamic Neural Generation Networks, by Shujian Zhang et al.
Switchable Decision: Dynamic Neural Generation Networks
by Shujian Zhang, Korawat Tanwisuth, Chengyue Gong, Pengcheng He, Mingyuan Zhou
First submitted to arxiv on: 7 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 proposed dynamic neural generation networks offer a novel approach to accelerate inference in auto-regressive generation models, which are commonly used in various natural language processing (NLP) tasks such as summarization, question answering, and classification. These models have shown competitive performance across many NLP tasks but struggle with slow inference times, making real-time deployment challenging. To address this limitation, the authors introduce a switchable decision mechanism that dynamically assigns computation resources for each data instance based on constrained optimization. This approach enables efficient inference paths while maintaining accuracy. Experimental results demonstrate the effectiveness of this method in reducing computation cost during inference without compromising performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to speed up computer programs that generate text, such as summarizing articles or answering questions. These programs are good at their job but can be slow and take a long time to process information. The authors came up with an idea to make them faster by deciding which parts of the program to use for each piece of information they’re processing. This lets them balance how much time they spend on each part while still getting accurate results. They tested this approach on several tasks, like answering questions and summarizing text, and found that it worked well. |
Keywords
» Artificial intelligence » Classification » Inference » Natural language processing » Nlp » Optimization » Question answering » Summarization
