Summary of On Speculative Decoding For Multimodal Large Language Models, by Mukul Gagrani et al.
On Speculative Decoding for Multimodal Large Language Models
by Mukul Gagrani, Raghavv Goel, Wonseok Jeon, Junyoung Park, Mingu Lee, Christopher Lott
First submitted to arxiv on: 13 Apr 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 abstract discusses the challenges of inference with Multimodal Large Language Models (MLLMs), specifically the LLaVA 7B model, due to memory bandwidth bottlenecks and auto-regressive token generation. To address this issue, the authors explore speculative decoding, a technique that utilizes a language-only model as a draft model to bypass image tokens and their associated processing components. The results show that speculative decoding can achieve a memory-bound speedup of up to 2.37x using a 115M parameter language model trained from scratch. Additionally, the authors introduce a compact LLaVA draft model incorporating an image adapter, which shows marginal performance gains in image captioning while maintaining comparable results in other tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making large language models work faster and more efficiently. Right now, these models are slow because they have to process lots of information and generate new tokens one by one. The authors found a way to make them work better using something called speculative decoding. They tested this method on three different tasks and showed that it can make the model run up to 2.37 times faster. They also created a smaller version of the language model that is still good at doing certain tasks, like captioning images. |
Keywords
» Artificial intelligence » Image captioning » Inference » Language model » Token
