Summary of Tandem Transformers For Inference Efficient Llms, by Aishwarya P S and Pranav Ajit Nair and Yashas Samaga and Toby Boyd and Sanjiv Kumar and Prateek Jain and Praneeth Netrapalli
Tandem Transformers for Inference Efficient LLMs
by Aishwarya P S, Pranav Ajit Nair, Yashas Samaga, Toby Boyd, Sanjiv Kumar, Prateek Jain, Praneeth Netrapalli
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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 This paper addresses a fundamental limitation of conventional large language models (LLMs), which rely on sequential token generation. The autoregressive nature of these models slows down inference, hindering their real-world applications. The authors propose innovative techniques for speculative and parallel decoding to accelerate inference, building upon the strengths of base LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are slow because they generate tokens one by one. This makes them not very good for tasks that need quick answers. Some people try to fix this by using smaller models or guessing what the next token is. But these methods have their own problems. They’re either not as accurate or don’t use the original model’s information well. |
Keywords
» Artificial intelligence » Autoregressive » Inference » Token
