Summary of Extracting Prompts by Inverting Llm Outputs, By Collin Zhang et al.
Extracting Prompts by Inverting LLM Outputs
by Collin Zhang, John X. Morris, Vitaly Shmatikov
First submitted to arxiv on: 23 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 The paper addresses the problem of language model inversion, aiming to recover the original prompt from given model outputs. The authors propose a novel black-box method, output2prompt, that can extract prompts without access to the model’s internal workings or requiring adversarial queries. Unlike previous methods, output2prompt only relies on normal user query outputs. To enhance memory efficiency, it employs a new sparse encoding technique. The paper demonstrates the effectiveness of output2prompt on various user and system prompts, showcasing zero-shot transferability across different large language models (LLMs). This work has implications for understanding how LLMs process user input and potentially improving their performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to figure out what someone was asking a computer chatbot just by looking at the bot’s response. That’s basically what this paper is about – finding a way to reverse-engineer what people were saying to a language model, given only its response. The researchers created a new method called output2prompt that can do this without knowing any secrets about how the model works or needing special tricks. They tested it on different types of prompts and showed that it can work well even when trying out new models. This could help us better understand how language models process what we’re saying to them. |
Keywords
» Artificial intelligence » Language model » Prompt » Transferability » Zero shot
