Summary of Characterizing Prompt Compression Methods For Long Context Inference, by Siddharth Jha et al.
Characterizing Prompt Compression Methods for Long Context Inference
by Siddharth Jha, Lutfi Eren Erdogan, Sehoon Kim, Kurt Keutzer, Amir Gholami
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 paper presents a comprehensive evaluation of different prompt compression methods for long context inference tasks. Recent approaches have attempted to compress prompts to reduce the context length and improve accuracy, but there has been little comparison across various tasks. This study analyzes extractive compression, summarization-based abstractive compression, and token pruning methods, revealing that extractive compression often outperforms others with minimal accuracy degradation. The results show up to 10x compression and marginal improvements on summarization tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper compares different ways to shorten prompts for computer programs to understand longer texts. This helps make the programs more efficient and accurate. Researchers tried different methods, but no one compared them all before. In this study, they look at three main approaches: taking part of the prompt, making a summary of it, and removing unnecessary words. Surprisingly, taking part of the prompt often works best and can shorten texts by up to 10 times without losing accuracy. The other methods didn’t work as well. |
Keywords
» Artificial intelligence » Context length » Inference » Prompt » Pruning » Summarization » Token
