Summary of Can Llms Separate Instructions From Data? and What Do We Even Mean by That?, By Egor Zverev et al.
Can LLMs Separate Instructions From Data? And What Do We Even Mean By That?
by Egor Zverev, Sahar Abdelnabi, Soroush Tabesh, Mario Fritz, Christoph H. Lampert
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 introduces a formal measure and benchmark to quantify the instruction-data separation problem in Large Language Models (LLMs). This issue makes them vulnerable to manipulations, making them unsuitable for safety-critical tasks. The authors present a new dataset, SEP, to estimate the measure for real-world models. They also show that all LLMs fail to achieve high separation and canonical mitigation techniques either fail or reduce model utility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers try to solve a problem with big language models. These models are great at doing things like answering questions and generating text, but they can be tricked into doing the wrong thing. The authors want to fix this by creating a way to measure how well these models keep their instructions separate from their data. They also create a new dataset that can help them test real-world models. |
