Summary of Spml: a Dsl For Defending Language Models Against Prompt Attacks, by Reshabh K Sharma and Vinayak Gupta and Dan Grossman
SPML: A DSL for Defending Language Models Against Prompt Attacks
by Reshabh K Sharma, Vinayak Gupta, Dan Grossman
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Cryptography and Security (cs.CR); Programming Languages (cs.PL)
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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 proposes a novel approach to prevent malicious applications of large language models (LLMs) in chatbot design. The authors introduce System Prompt Meta Language (SPML), a domain-specific language that refines prompts, monitors user inputs, and prevents attacks on LLM-based chatbots. SPML actively checks for attack prompts, ensuring they align with chatbot definitions, thus optimizing costs. Additionally, the paper presents a benchmark dataset of 1.8k system prompts and 20k user inputs to evaluate chatbot definition performance. The authors demonstrate SPML’s effectiveness in understanding attacker prompts, outperforming models like GPT-4, GPT-3.5, and LLAMA. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to stop bad people from using big language models to create chatbots that can be harmful. They created a special language called SPML that helps make sure the chatbot definitions are good and don’t get changed by someone trying to cause trouble. This makes it more expensive for bad people to try and do something mean. The paper also includes a big dataset of examples to help test how well this works. |
Keywords
* Artificial intelligence * Gpt * Llama * Prompt
