Summary of Language Model Prompt Selection Via Simulation Optimization, by Haoting Zhang et al.
Language Model Prompt Selection via Simulation Optimization
by Haoting Zhang, Jinghai He, Rhonda Righter, Zeyu Zheng
First submitted to arxiv on: 12 Apr 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 proposes a two-stage framework for selecting prompts that maximize a pre-defined score for generative language models. The first stage determines a feasible set of prompts using moderate-dimensional vectors, while the second stage constructs a surrogate model to evaluate and select the best prompt. The authors prove the consistency of their sequential evaluation procedure and demonstrate its efficacy through numerical experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re helping an AI generate text by giving it instructions or ideas. This paper is about finding the best way to give those instructions, called “prompts,” so that the AI generates great content. Right now, people are doing this job themselves, but the researchers want to make a machine do it for them. They came up with a two-step process: first, they’ll come up with lots of possible prompts and then choose the best one based on how well it works. |
Keywords
» Artificial intelligence » Prompt
