Social Foundations of Large Language Models

Guanhua Zhang (Ph.D. Student)

This research project aims to enhance the understanding of the broader societal impacts and challenges associated with large language models (LLMs) such as OpenAI’s GPT-4. The study investigates the existing misalignment between LLMs and human society, and proposes efficient methods for LLMs to learn from human feedback, enabling them to acquire knowledge and capture human preferences more effectively. Additionally, the project explores the potential impacts of regular users on LLMs and develops algorithms that allow for collective correction to tackle potential ethical and legal dilemmas of LLMs. Furthermore, a key objective is to elucidate the long-term effects surrounding LLMs, particularly by examining the intricate feedback loops between humans and LLMs. In doing so, this research aims to develop methods that ensure LLMs evolve in a manner that promotes the communal welfare of humanity. By bridging the gap between human society and LLMs, this project seeks to address the ethical, legal, and social implications that may arise and strives for the responsible integration of LLMs into human environments.

Primary Host:	Moritz Hardt (Max Planck Institute for Intelligent Systems)
Exchange Host:	Martin Jaggi (EPFL)
PhD Duration:	01 August 2023 - 01 August 2026
Exchange Duration:	01 February 2026 - 01 August 2026 - Ongoing