With the rapid development of artificial intelligence technology, large language models (LLM) have an increasingly profound impact on society. How to ensure that these powerful tools are consistent with human values has become an important research direction. This paper introduces a new method called OPO, which is able to dynamically align the values of large models in real time without retraining the model. The method is simple and easy to use, suitable for large models of both closed and open source, and provides breakthrough progress in the alignment of legal and ethical standards.
With the development of artificial intelligence technology, large language models represented by GPT-4 are having a profound impact on society with their powerful capabilities. The new method OPO does not require retraining the model, dynamically aligning values in real time, and the alignment method is convenient and fast. Researchers use the OPO method to align large models with legal and ethical standards. The security issue of the large model itself has become important, and breakthroughs have been made in real-time dynamic alignment of values. As a result, the OPO method does not require training and is applicable to both closed-source and open-source large models. The OPO code has been made public on GitHub, and the researchers built three test benchmarks annotated by humans and two test benchmarks automatically generated by the model.
The emergence of the OPO method provides a new idea for solving the value alignment problem of large language models, and its efficiency and applicability deserve attention. In the future, methods like OPO may become an important tool to ensure the safe and reliable development of AI. The open source of this method also promotes cooperation between academia and industry to jointly promote the healthy development of AI technology.