WorkDifferentWithAI.com Academic Paper Alert!
Written by Rimon Melamed, Lucas H. McCabe, Tanay Wakhare, Yejin Kim, H. Howie Huang, Enric Boix-Adsera
Category: AI for IT
Article Section: AI Development and Operations; AI-Assisted Programming
Publication Date: 2023-11-12
SEO Description: “Introducing PROPANE: A novel framework for automatic prompt optimization in Large Language Models without user intervention.”
AI-Generated Paper Summary
The academic paper titled “PROPANE: Prompt design as an inverse problem,” authored by Rimon Melamed, Lucas H. McCabe, Tanay Wakhare, Yejin Kim, and H. Howie Huang, Enric Boix-Adsera, tackles the challenge of prompt design for Large Language Models (LLMs). It introduces an automated framework known as PROPANE, which is focused on the optimization of prompts to elicit semantically similar responses from LLMs to a given set of examples, sans manual intervention. The authors demonstrate that their proposed system not only enhances the performance of existing prompts but also has the capability to generate semantically obfuscated prompts that are transferable across different models. The paper, consisting of 27 pages and 11 figures, is a preprint submission under the subject classification of Computation and Language (cs.CL) on arXiv and outlines substantial advances in the field of language model prompt engineering.
Here is a brief analysis of the key points and potential applications of this research paper:
- Formulates prompt engineering as an inverse problem, where the goal is to reconstruct a prompt that induces similar statistical behavior as an unknown “ground truth” prompt. This is a new perspective on prompt design.
- Proposes an optimization framework called PROPANE to solve this inverse prompt reconstruction problem by minimizing the KL divergence between the prompt distributions.
- Finds that PROPANE can uncover “obfuscated” prompts that are not human interpretable but produce similar model outputs. These obfuscated prompts also transfer between different model sizes.
- PROPANE could be used by companies to reconstruct prompts for proprietary systems given sample conversations, to create alternative prompts.
- It could help make prompts more robust and compress prompts by finding a few “special tokens” that drive model behavior.
- The obfuscated prompts may have intellectual property value if they reliably control model behavior but are hard to reverse engineer.
- PROPANE provides a way to quantitatively evaluate prompts based on the KL divergence, rather than human judgment. This could be used to benchmark prompt engineering techniques.
- The framework could be extended for style transfer, data compression, or other conditional text generation tasks that rely on effective prompting.
In summary, this paper introduces a novel optimization view of prompt design and demonstrates promising results on reconstructing and analyzing prompts. The proposed PROPANE system has potential commercial value for tasks relying on prompt engineering.
PROPANE, Prompt design, inverse problem, Large Language Models, semantically obfuscated prompts
Carefully-designed prompts are key to inducing desired behavior in Large Language Models (LLMs). As a result, great effort has been dedicated to engineering prompts that guide LLMs toward particular behaviors. In this work, we propose an automatic prompt optimization framework, PROPANE, which aims to find a prompt that induces semantically similar outputs to a fixed set of examples without user intervention. We further demonstrate that PROPANE can be used to (a) improve existing prompts, and (b) discover semantically obfuscated prompts that transfer between models.