Selective Generalization: Improving Capabilities While Maintaining Alignment
As artificial intelligence (AI) continues to advance, there is a growing concern about the risks associated with its development and deployment. One of these risks is misgeneralization, where AI models produce outputs that are not aligned with human values or ethics. In this article, we will explore the concept of selective generalization and its implications for improving capabilities while maintaining alignment.
The Problem of Misalignment
Misalignment occurs when an AI model produces outputs that are not aligned with human values or ethics. This can happen in various contexts, such as medical advice, financial planning, or even conversation. The problem is further exacerbated by the fact that many AI models are trained on large datasets, which can contain biases and inconsistencies.
The Need for Selective Generalization
Selective generalization refers to the process of training AI models on limited data while ensuring that they generalize well across various contexts. This is a crucial aspect of developing reliable and trustworthy AI systems.
Our Approach
We have developed a framework for selective generalization using proxy alignment data. Our approach involves training AI models on limited alignment data, which includes a small subset of the training data that is carefully curated to ensure that it is representative of the desired behavior.
Experimental Results
Our experiments demonstrate that our approach can improve capabilities while maintaining alignment. We used a range of datasets and evaluation metrics to assess the performance of our models, including medical advice, financial planning, and conversation.
Pareto Plot: Alignment vs. Task Performance
We created a Pareto plot to visualize the trade-off between alignment and task performance. The plot shows that there is a consistent trade-off between these two metrics, with some methods performing better on one metric at the expense of the other.
Safe LoRA: A Method for Selective Generalization
We also developed a method called Safe LoRA (Safe Linear Regression and Alignment) which aims to minimize interference between task data and alignment data. Our results show that Safe LoRA can be effective in preventing misalignment, but its performance depends on the quality of the proxy data.
We also created simple toys and games to demonstrate some of the phenomena we observed in our experiments. These include a game where a model is trained on positive emotions triggers, which leads to catastrophic forgetting, and another game where a model is fine-tuned on a small proxy dataset, leading to a spillover effect.
Selective generalization is an essential aspect of developing reliable and trustworthy AI systems. Our approach using proxy alignment data has shown promising results in improving capabilities while maintaining alignment. However, there is still much work to be done to develop methods that can effectively mitigate misalignment. We hope that our findings will contribute to the ongoing discussion about the risks and benefits of AI development.
We plan to continue exploring new methods for selective generalization and investigating their performance on various datasets and evaluation metrics. We also aim to work with practitioners and researchers to develop practical solutions that can be deployed in real-world applications.
References:
[1] Ariana Azarbal, Matthew A. Clarke, Jorio Cocola, Cailley Factor, and Alex Cloud (2025). Selective Generalization: Improving Capabilities While Maintaining Alignment. arXiv preprint arXiv:2109.04855.
Appendix
The appendix includes detailed descriptions of the experiments, methods, and results, as well as additional information on the toy models and games used to demonstrate some of the phenomena observed in our experiments.