Summary of Symbolic Prompt Program Search: a Structure-aware Approach to Efficient Compile-time Prompt Optimization, by Tobias Schnabel et al.
Symbolic Prompt Program Search: A Structure-Aware Approach to Efficient Compile-Time Prompt Optimization
by Tobias Schnabel, Jennifer Neville
First submitted to arxiv on: 2 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper addresses the pressing issue of optimizing prompt programs in modern Large Language Model (LLM) applications, where prompts have become autonomous programs themselves. This optimization is crucial for tasks such as retrieval-augmented generation, which rely on repeated calls to these prompt programs with varying user queries or data instances. The authors highlight that recent work has primarily focused on either simple prompt programs or assumed a fixed general structure, neglecting the complexity of real-world applications. To bridge this gap, the paper proposes novel techniques for optimizing prompt programs, leveraging advancements in model-free optimization and transfer learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In many modern computer systems, prompts are used to ask questions and get answers from large language models. These prompts become like mini-programs that need to be optimized so they can give better responses. Right now, people are still figuring out how to do this well. Some research has looked at simple prompt programs or assumed that the overall structure of a prompt program stays the same. But real-world applications are more complicated than that. This paper tries to solve this problem by developing new ways to optimize prompts using special techniques and learning from other models. |
Keywords
» Artificial intelligence » Large language model » Optimization » Prompt » Retrieval augmented generation » Transfer learning
