Social Learning: Towards Collaborative Learning with Large Language Models (2312.11441v2)

Abstract: We introduce the framework of "social learning" in the context of LLMs, whereby models share knowledge with each other in a privacy-aware manner using natural language. We present and evaluate two approaches for knowledge transfer between LLMs. In the first scenario, we allow the model to generate abstract prompts aiming to teach the task. In our second approach, models transfer knowledge by generating synthetic examples. We evaluate these methods across diverse datasets and quantify memorization as a proxy for privacy loss. These techniques inspired by social learning yield promising results with low memorization of the original data. In particular, we show that performance using these methods is comparable to results with the use of original labels and prompts. Our work demonstrates the viability of social learning for LLMs, establishes baseline approaches and highlights several unexplored areas for future work.

• The paper introduces a social learning framework where large language models exchange information in natural language while adhering to strict privacy protocols.
• It evaluates two key methods—abstract prompts and synthetic examples—for transferring knowledge effectively across varied datasets.
• Experimental results show that social learning achieves competitive performance compared to private training while reducing the risk of memorizing sensitive information.

Introduction

LLMs are increasingly central to real-world applications as they adapt to environmental cues and serve as tools for various tasks such as providing assistance via chatbots. However, when these models interact as a network of agents—such as in collaborative spam detection—a major challenge arises: how can they share useful information without compromising user privacy?

Social Learning Framework

The paper introduces a framework named "social learning" in the context of LLMs. This framework allows models to educate each other using natural language while adhering to privacy protocols. The research explores two methods of knowledge transfer. One involves the generation of abstract prompts that convey the nature of the task, whereas the other focuses on the generation of synthetic examples as learning aids. Both methods are evaluated for their effectiveness across different datasets while also assessing privacy implications by quantifying data memorization.

Methods and Experimentation

The concept of social learning is operationalized using an environment where multiple teacher agents teach a student agent about a task while maintaining privacy. Two distinct operational modes are considered: training and inference. During training, the student learns from teachers through textual exchanges; at inference time, the student utilizes this knowledge to answer queries. Social learning's effectiveness is tested on datasets that challenge various aspects of language comprehension and reasoning.

Results and Privacy Analysis

The paper finds that performance using social learning methods is often comparable to directly utilizing private examples or tasks. Teachers rarely duplicate private examples verbatim, and the generated examples or instructions usually have a significant distance from the original data, implying that they are sufficiently unique. Nonetheless, privacy concerns are assessed further with the adapted Secret Sharer metric, which reveals cases of subtle memorization of private data, suggesting a need for better privacy-preserving mechanisms.

Conclusions and Future Directions

This paper establishes social learning as a viable approach for collaborative learning by LLMs. It sets benchmarks for this paradigm and opens avenues for future research, which may include refining the teaching process, exploring applications to other modalities, and developing mechanisms to guarantee more robust privacy protections.