Summary of Pld+: Accelerating Llm Inference by Leveraging Language Model Artifacts, By Shwetha Somasundaram et al.
PLD+: Accelerating LLM inference by leveraging Language Model Artifacts
by Shwetha Somasundaram, Anirudh Phukan, Apoorv Saxena
First submitted to arxiv on: 2 Dec 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 Speculative decoding has emerged as a novel approach to reduce latency associated with autoretrogressive LLM inference, where future tokens are drafted and verified in parallel. However, its practical deployment is hindered by the need for additional computational resources and fine-tuning, limiting its out-of-the-box usability. To address these challenges, the authors present PLD+, a suite of novel algorithms designed to accelerate LLM inference, particularly for input-guided tasks such as code editing, text editing, summarization, and others. These tasks feature outputs with substantial overlap with their inputs, which PLD+ exploits. The approach also leverages artifacts generated during inference, including attention and hidden states, to accelerate inference speed. Experimental results on five input-guided tasks show that PLD+ outperforms all tuning-free approaches, even surpassing the state-of-the-art tuning-dependent approach EAGLE in the greedy setting (achieving an average speedup of up to 2.31). The authors’ approach is tuning free and does not require additional compute, making it easily applicable for accelerating inference of any LLM. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make language models work faster has been developed. Right now, language models take a while to understand what you’re saying because they have to think ahead about what might happen next. This makes them slow and not very useful. The authors of this paper have come up with some clever ideas to speed up the process without needing extra computer power or special training. They tested their approach on five different tasks that involve changing text, like editing code or summarizing a long piece of writing. Their method was faster than other ways of doing things and even beat the best existing method in most cases. This is exciting because it means language models can be used for many more applications where speed is important. |
Keywords
» Artificial intelligence » Attention » Fine tuning » Inference » Summarization
