Similar Thinking in Large Language Models Jeopardizes AI Oversight

The rapid development of large language models (LLMs) poses increasing challenges for human oversight. The sheer volume of generated text and the increasing complexity of the models make evaluation and control by human experts difficult. A promising approach to solving this problem is so-called "AI oversight," where other language models automate the evaluation and training of LLMs.

A new study now investigates how the similarity between models affects AI oversight. The researchers propose a probabilistic metric that measures the similarity of LLMs based on the overlap of their errors. Using this metric, they were able to show that LLMs, when acting as evaluators, tend to favor models that are similar to themselves. This result confirms previous observations regarding the self-preference of LLMs.

Furthermore, the study investigated the influence of model similarity on the training of LLMs with annotations generated by other LLMs. It showed that complementary knowledge between the weaker "teacher" model and the stronger "student" model plays a crucial role in learning success. The greater the differences in the models' knowledge, the more effective the knowledge transfer.

As LLMs become more powerful, it becomes more difficult to identify their errors. This leads to an increased reliance on AI oversight. However, the study reveals a worrying trend: the errors of powerful models are increasingly similar. These correlated errors carry the risk of systematic malfunctions and make it more difficult to identify and correct problems.

The Importance of Model Diversity

The results of the study underscore the importance of considering and correcting for model similarities, especially in the context of AI oversight. High diversity in the models used is essential to minimize the risk of correlated errors and to ensure comprehensive evaluation and control of LLMs. The development of methods for measuring and controlling model diversity is therefore an important step for the safe and responsible development and application of AI.

The increasing complexity and performance of LLMs requires new approaches to quality assurance and control. While AI oversight offers promising possibilities, it also presents new challenges. Considering model similarity is a crucial factor for the success of these approaches and the avoidance of systematic errors.

Further research is necessary to better understand the causes and effects of model similarity and to develop effective strategies to promote model diversity. Only then can AI oversight reach its full potential and ensure the safe and responsible development of LLMs.

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