Amirkeivan Mohtashami, a Research Intern, and Florian Hartmann, a Software Engineer at Google Research, discussed the potential of large language models (LLMs) to learn from each other in a social learning framework. They explored the concept of social learning and how it could benefit LLMs in improving their performance. The idea is inspired by how humans learn from each other in social settings.
The researchers outlined a framework where LLMs can share knowledge with each other using natural language in a privacy-aware manner. They conducted experiments to evaluate the effectiveness of this approach on various tasks and proposed methods to measure privacy in this setting. Unlike traditional collaborative learning methods that rely on gradients, the researchers focused on teaching agents purely using natural language.
In their study, the researchers considered a scenario where a student LLM learns from multiple teacher entities that already know the task at hand. They evaluated the student’s performance on tasks like spam detection, solving math problems, and answering questions based on text. The social learning process involved teachers providing instructions or few-shot examples to the student without sharing private data.
To address privacy concerns, the researchers also explored the use of synthetic examples generated by teachers to teach students. They found that these synthetic examples could effectively improve performance without compromising privacy. Additionally, they investigated the use of generated instructions for tasks where providing examples was not feasible.
The researchers also developed a metric, Secret Sharer, to measure data leakage during the social learning process. Their results showed that the student model was only slightly more confident in the data points used by teachers, indicating that the process of teaching was effective without revealing specifics from the original data.
In conclusion, the researchers introduced a framework for social learning among LLMs to transfer knowledge while preserving data privacy. They are now exploring ways to enhance the teaching process and expanding social learning to modalities beyond text. The study was a collaborative effort involving several researchers and contributors, and their findings have the potential to advance the field of natural language processing.
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