Summary of Progressgym: Alignment with a Millennium Of Moral Progress, by Tianyi Qiu et al.
ProgressGym: Alignment with a Millennium of Moral Progress
by Tianyi Qiu, Yang Zhang, Xuchuan Huang, Jasmine Xinze Li, Jiaming Ji, Yaodong Yang
First submitted to arxiv on: 28 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose a novel approach to mitigating the risks associated with large language models (LLMs) influencing human moral beliefs. The authors introduce “progress alignment” algorithms that learn to emulate the mechanics of human moral progress, addressing the limitations of existing alignment methods. To facilitate research in this area, they develop ProgressGym, an experimental framework that allows for learning moral progress mechanics from historical text and 18 LLMs. This framework enables the creation of concrete benchmarks for tracking evolving values, preemptively anticipating moral progress, and regulating the feedback loop between human and AI value shifts. The authors also present baseline methods and invite novel algorithms and challenges through an open leaderboard. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can influence human moral beliefs and potentially lock in misguided moral practices. Researchers propose “progress alignment” to mitigate this risk by learning from 9 centuries of historical text and 18 LLMs. They develop ProgressGym, a framework for tracking evolving values, anticipating moral progress, and regulating feedback loops. The goal is to facilitate better decision-making. |
Keywords
» Artificial intelligence » Alignment » Tracking
