Summary of Context-aware Assistant Selection For Improved Inference Acceleration with Large Language Models, by Jerry Huang et al.
Context-Aware Assistant Selection for Improved Inference Acceleration with Large Language Models
by Jerry Huang, Prasanna Parthasarathi, Mehdi Rezagholizadeh, Sarath Chandar
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach is proposed to overcome the resource constraints associated with large language models (LLMs), which remain a barrier to their widespread adoption. The high latency of auto-regressive generation makes it challenging to use these models without advanced computing infrastructure. Assisted decoding has been used to alleviate this issue, but it relies on alignment between the draft and target models. To improve performance, multiple draft models can be used, but selecting an assistant model without knowledge of its construction is difficult. A contextual bandit framework is applied to address this decision-making problem, where a policy must choose a draft model based on context. The results show that even without prior knowledge of the draft models, training a policy over the alignment of their outputs can accelerate performance on multiple domains. This approach holds promise for various settings with multiple assisted decoding candidates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are very powerful tools, but they require a lot of computer power to work well. One problem is that it takes a long time to generate text using these models. To fix this, scientists have been using smaller “draft” models to help the larger models do their job. But this only works if the draft model is good at doing what the bigger model does. If the draft model isn’t good enough, things don’t work well. A new way of thinking about this problem is like a game where you have to choose which draft model to use based on the situation. The scientists found that by using data from when these draft models worked together with the big models, they could make better decisions and get faster results. |
Keywords
» Artificial intelligence » Alignment
