Summary of Taco-rl: Task Aware Prompt Compression Optimization with Reinforcement Learning, by Shivam Shandilya et al.
TACO-RL: Task Aware Prompt Compression Optimization with Reinforcement Learning
by Shivam Shandilya, Menglin Xia, Supriyo Ghosh, Huiqiang Jiang, Jue Zhang, Qianhui Wu, Victor Rühle
First submitted to arxiv on: 19 Sep 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 proposed reinforcement learning-based prompt compression method addresses the challenges of large language models in various applications. The increasing size of prompts required for optimal performance leads to computational efficiency issues, which existing methods fail to adequately address. The new approach leverages a Transformer encoder-based token classification model and guides the learning process with task-specific reward signals using the REINFORCE algorithm. This method improves task performance by 8%-189% on three diverse tasks (text summarization, question answering, and code summarization) while satisfying compression rate and latency requirements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to make language models more efficient. Right now, we need huge prompts to get good results, but that takes a lot of computer power. The researchers created a special way to shrink these prompts without losing performance. They used a machine learning algorithm to figure out which parts of the prompt are most important and can be safely removed. This new method worked really well on three different tasks: summarizing text, answering questions, and summarizing code. It even did better than other methods that tried to do something similar! |
Keywords
» Artificial intelligence » Classification » Encoder » Machine learning » Prompt » Question answering » Reinforcement learning » Summarization » Token » Transformer
