Summary of Think Big, Generate Quick: Llm-to-slm For Fast Autoregressive Decoding, by Benjamin Bergner et al.
Think Big, Generate Quick: LLM-to-SLM for Fast Autoregressive Decoding
by Benjamin Bergner, Andrii Skliar, Amelie Royer, Tijmen Blankevoort, Yuki Asano, Babak Ehteshami Bejnordi
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 research paper proposes a hybrid approach to improve the efficiency of large language models (LLMs) while maintaining their high performance. The authors combine an encoder-decoder LLM with a small language model (SLM), which is trained on the representations generated by the LLM. This allows for faster generation tasks, such as translation and summarization, while only requiring minor fine-tuning of the SLM. The method is tested on various benchmarks, achieving substantial speedups of up to 4x with minimal performance penalties. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes big language models more efficient! It combines a big model with a smaller one to make it faster and cheaper to use. This helps with tasks like translating text or summarizing articles. The new method is tested on different datasets and shows speedups of up to 4 times, which is pretty impressive. |
Keywords
* Artificial intelligence * Encoder decoder * Fine tuning * Language model * Summarization * Translation
