Summary of Uncovering Hidden Intentions: Exploring Prompt Recovery For Deeper Insights Into Generated Texts, by Louis Give et al.
Uncovering Hidden Intentions: Exploring Prompt Recovery for Deeper Insights into Generated Texts
by Louis Give, Timo Zaoral, Maria Antonietta Bruno
First submitted to arxiv on: 22 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper explores the novel idea of recovering the prompt used to generate AI-produced content. It presents the first investigation in this domain, departing from traditional detection methods and focusing on a specific set of tasks. The researchers employ zero-shot and few-shot in-context learning as well as LoRA fine-tuning to study the feasibility of prompt recovery. They also create a semi-synthetic dataset to evaluate its benefits. Limiting their study to text generated by a single model, they demonstrate that it is possible to accurately recover the original prompt. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to solve a new problem: figuring out what someone wrote that made a computer generate certain text. They’re not just looking for fake content – they want to know what was written in the first place. To do this, they use some clever techniques like learning from examples and fine-tuning their approach. They even make some of their own data to test how well it works. Surprisingly, they were able to accurately guess what someone wrote that made a computer generate certain text. |
Keywords
» Artificial intelligence » Few shot » Fine tuning » Lora » Prompt » Zero shot
