Summary of A Comprehensive Survey Of Accelerated Generation Techniques in Large Language Models, by Mahsa Khoshnoodi et al.
A Comprehensive Survey of Accelerated Generation Techniques in Large Language Models
by Mahsa Khoshnoodi, Vinija Jain, Mingye Gao, Malavika Srikanth, Aman Chadha
First submitted to arxiv on: 15 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A comprehensive survey is presented on accelerated generation techniques in autoregressive language models, exploring state-of-the-art methods and their applications. The paper categorizes these techniques into speculative decoding, early exiting mechanisms, and non-autoregressive methods, discussing underlying principles, advantages, limitations, and recent advancements. This survey aims to provide insights into the current landscape of LLMs and guide future research directions in natural language processing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are essential for generating text quickly, but their sequential nature causes high inference latency, hindering real-time applications. To address this, various techniques have been developed. This survey looks at different methods to speed up generation, such as speculative decoding and early exiting mechanisms. It also explores non-autoregressive approaches that can generate text in parallel. |
Keywords
» Artificial intelligence » Autoregressive » Inference » Natural language processing
