Summary of Nevermind: Instruction Override and Moderation in Large Language Models, by Edward Kim
Nevermind: Instruction Override and Moderation in Large Language Models
by Edward Kim
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this study, researchers investigate and compare the performance of various large language models (LLMs) in following explicit instructions, particularly when there are conflicting situations or overrides. They examine how different-sized open-source and proprietary models handle tasks such as overriding internal knowledge, moderating extracted information, and performing full jailbreaks. The findings suggest that larger models excel at following instructions that override internal and contextual knowledge, but caution is needed to prevent over-reliance on these capabilities. Additionally, the study highlights the importance of maintaining a buffer zone around the perplexity cliff when scaling up contexts through rope scaling. Furthermore, it reveals an inherent tension between improving instruction following and adhering to safety filters or guidelines for trustworthy AI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how big language models do when we tell them what to do, especially when there are conflicts or overrides. Scientists tested many different models, from open-source ones to special ones made by companies. They found that bigger models are better at following instructions that override their own ideas and the context around them. However, this also means they might be too good at following orders, so we need to be careful. The study also shows that when we make the language model understand longer texts, we need to leave a little space between the safe zone and the “perplexity cliff” so the model doesn’t get confused. Overall, it’s hard to balance improving instruction following with keeping AI safe. |
Keywords
* Artificial intelligence * Language model * Perplexity
