Summary of Efficient Prompt Optimization Through the Lens Of Best Arm Identification, by Chengshuai Shi et al.
Efficient Prompt Optimization Through the Lens of Best Arm Identification
by Chengshuai Shi, Kun Yang, Zihan Chen, Jundong Li, Jing Yang, Cong Shen
First submitted to arxiv on: 15 Feb 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 Large language models (LLMs) have demonstrated impressive instruction-following capabilities, sparking interest in optimizing prompts for these models. Most existing approaches generate a pool of candidate prompts and then select the best one, but these designs often neglect the cost of selecting and evaluating responses. To address this limitation, this paper presents TRIPLE, a principled framework that efficiently selects prompts under budget constraints by leveraging the connection between prompt optimization and multi-armed bandits. Experimental results on multiple tasks using various LLMs show significant performance improvements while satisfying budget constraints. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart computers that can understand and follow instructions. But to get the best out of them, we need to find the right prompts to give them. Most people do this by choosing from a list of pre-made prompts, but they don’t think about how much it costs to try out each one. This paper helps fix this problem by creating a special system called TRIPLE that can pick the best prompt quickly and within a budget. |
Keywords
* Artificial intelligence * Optimization * Prompt
