Summary of Intent-based Prompt Calibration: Enhancing Prompt Optimization with Synthetic Boundary Cases, by Elad Levi et al.
Intent-based Prompt Calibration: Enhancing prompt optimization with synthetic boundary cases
by Elad Levi, Eli Brosh, Matan Friedmann
First submitted to arxiv on: 5 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 This paper introduces a new method for automatic prompt engineering, which is essential for optimizing the performance of Large Language Models (LLMs). The proposed approach uses a calibration process that iteratively refines the prompt to align with user intent. This method is demonstrated to outperform state-of-the-art methods on real-world tasks such as moderation and generation, using strong proprietary models and limited annotated samples. The system’s modular design allows for easy adaptation to other tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers talk better by automatically creating the right questions to ask language models. It uses a special process that gets smarter and more accurate over time, making sure the computer asks the right questions to get the best answers. This is important because language models are very sensitive to the questions they’re asked, and getting it wrong can lead to bad results. The new method works well on real-world tasks like moderating online conversations and generating text. |
Keywords
* Artificial intelligence * Prompt
