Summary of Joint Fine-tuning and Conversion Of Pretrained Speech and Language Models Towards Linear Complexity, by Mutian He et al.
Joint Fine-tuning and Conversion of Pretrained Speech and Language Models towards Linear Complexity
by Mutian He, Philip N. Garner
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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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 Cross-Architecture Layerwise Distillation (CALD), a novel approach to convert transformer models into linear time substitutes while fine-tuning them for target tasks. The authors demonstrate the effectiveness of CALD on various language processing, language modeling, and speech processing tasks, showcasing its ability to recover the results of original transformer models. To optimize the fine-tuning process, several strategies are proposed, including the use of target models and parameter trajectories. The paper explores the impact of these strategies on the inference capability retention from the original model, shedding light on the factors contributing to variation in performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to speed up powerful AI models without sacrificing their accuracy. That’s what this research is about. They created a new way to convert complex models into faster, more efficient versions while still maintaining their ability to perform well on specific tasks. The authors tested this method on various language-related tasks and found that it can successfully replicate the results of original models. This breakthrough has implications for improving AI performance in areas like speech recognition, natural language processing, and more. |
Keywords
» Artificial intelligence » Distillation » Fine tuning » Inference » Natural language processing » Transformer
