Summary of Multitask Mayhem: Unveiling and Mitigating Safety Gaps in Llms Fine-tuning, by Essa Jan et al.
Multitask Mayhem: Unveiling and Mitigating Safety Gaps in LLMs Fine-tuning
by Essa Jan, Nouar AlDahoul, Moiz Ali, Faizan Ahmad, Fareed Zaffar, Yasir Zaki
First submitted to arxiv on: 18 Sep 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 This paper investigates the impact of fine-tuning Large Language Models (LLMs) on their safety performance across various downstream tasks, such as summarization, code generation, translation, and classification. The study reveals that fine-tuning LLMs for certain tasks like code generation and translation leads to significant degradation in safety guardrails. Additionally, the results show that LLMs have weaker guardrails for translation and classification, with a high percentage of harmful prompts answered correctly. The paper also highlights the limitations of current solutions, including guards and safety tuning datasets, which lack cross-task robustness. To address these issues, the authors developed a new multitask safety dataset that effectively reduces attack success rates across various tasks without compromising the model’s overall helpfulness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how fine-tuning big language models affects their ability to make good choices. The research shows that fine-tuning for certain tasks makes the models worse at avoiding bad things. It also finds that these models are not very good at stopping bad prompts from being answered correctly. The paper says that current methods for making sure the models behave well don’t work across all tasks. To fix this, the researchers created a new dataset that helps models make better choices without losing their ability to be helpful. |
Keywords
» Artificial intelligence » Classification » Fine tuning » Summarization » Translation
