Large Language Models (LLMs) are models that have greatly improved our ability to solve tasks using natural language. These models are approaching human-level performance. Now, researchers are exploring whether LLMs can learn from each other through social learning, much like humans do.
In a study titled “Social Learning: Towards Collaborative Learning with Large Language Models”, researchers investigate how LLMs can share knowledge with each other using natural language. The goal is to improve the performance of these models by learning from each other.
One approach is for a “student” LLM to learn from multiple “teacher” LLMs that already know how to perform certain tasks. The student’s performance is evaluated on tasks like spam detection, math problems, and text-based questions.
One method involves teachers providing instructions or examples to the student without sharing private data. This approach, called social learning, allows the student to learn without compromising privacy.
Another method involves teachers generating new examples for the student to learn from. These synthetic examples are different enough from the original ones to preserve privacy while still enabling effective learning.
By using social learning, LLMs can benefit from each other’s knowledge without compromising privacy. This approach shows promise in improving the performance of these models. Future research will focus on further enhancing the teaching process.
Overall, social learning among LLMs shows potential for improving the capabilities of these models in a privacy-conscious manner. Researchers are continuing to explore ways to enhance this collaborative learning process.