Summary of Aegis: Online Adaptive Ai Content Safety Moderation with Ensemble Of Llm Experts, by Shaona Ghosh et al.
AEGIS: Online Adaptive AI Content Safety Moderation with Ensemble of LLM Experts
by Shaona Ghosh, Prasoon Varshney, Erick Galinkin, Christopher Parisien
First submitted to arxiv on: 9 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Computers and Society (cs.CY)
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 The paper addresses the growing concern about Large Language Models (LLMs) and generative AI being used unsafely. It highlights the lack of high-quality datasets and benchmarks for evaluating content safety risks. To address this, the authors propose a taxonomy of 13 critical risk categories and 9 sparse risk categories, as well as curate the AEGISSAFETYDATASET, which contains approximately 26,000 human-LLM interaction instances annotated according to the taxonomy. The dataset is intended for use by the research community to help benchmark LLM models for safety. To demonstrate the effectiveness of the dataset, multiple LLM-based safety models (named AEGISSAFETYEXPERTS) are instruction-tuned and shown to surpass or perform competitively with state-of-the-art LLM-based safety models and general-purpose LLMs. The models also exhibit robustness across multiple jail-break attack categories. Additionally, using the AEGISSAFETYDATASET during the LLM alignment phase does not negatively impact the performance of the aligned models on MT Bench scores. Finally, the authors propose a novel application of a no-regret online adaptation framework (AEGIS) for content moderation with an ensemble of LLM content safety experts in deployment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores ways to keep Large Language Models safe from misuse. It reveals that there aren’t many good datasets to test how well these models can detect dangerous content. To fix this, the researchers create a list of 13 important types of risk and 9 less common ones. They also make a dataset with over 26,000 examples of humans interacting with LLMs, labeled according to their safety taxonomy. This dataset will help scientists test how well different models can detect risky content. The authors show that their safety models are better than existing ones at detecting dangerous content and don’t get worse when they’re used in real-life situations. |
Keywords
* Artificial intelligence * Alignment
