Summary of Learning to Compress Prompt in Natural Language Formats, by Yu-neng Chuang et al.
Learning to Compress Prompt in Natural Language Formats
by Yu-Neng Chuang, Tianwei Xing, Chia-Yuan Chang, Zirui Liu, Xun Chen, Xia Hu
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 proposed Natural Language Prompt Encapsulation (Nano-Capsulator) framework compresses lengthy prompts into NL formatted Capsule Prompts, addressing limitations in existing soft prompt compression methods. The Nano-Capsulator optimizes a reward function to interact with the proposed semantics-preserving loss and length constraints, achieving transferability across diverse LLMs and datasets. By reducing original prompt lengths by 81.4%, decreasing inference latency up to 4.5x, and saving 80.1% of budget overheads, this work demonstrates improved performance and efficiency in deploying large language models for various NLP tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to make it easier and more efficient to use large language models for tasks like text analysis. The problem is that these models are good at processing multiple tasks, but they don’t work well with long pieces of text, take a long time to process, and cost too much to compute. To solve this, the team proposes a new way to compress prompts into shorter, more efficient forms that still provide useful information. This approach uses natural language prompts, which are compatible with the models, and allows for flexibility in imposing length constraints. The results show that this method can reduce prompt lengths by 81.4%, speed up processing by up to 4.5x, and save 80.1% of computational resources. |
Keywords
* Artificial intelligence * Inference * Nlp * Prompt * Semantics * Transferability
