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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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GrooveSquid.com Paper Summaries

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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