[Paper Review] Release Strategies and the Social Impacts of Language Models

Irene Solaiman, Miles Brundage+13 more|arXiv (Cornell University)|Aug 24, 2019

Topic Modeling8 references283 citations

TL;DR

The paper discusses OpenAI's GPT-2 release strategy, emphasizing staged releases, risk-benefit analyses, and partnership-based research to address social impacts and guide responsible publication.

ABSTRACT

Large language models have a range of beneficial uses: they can assist in prose, poetry, and programming; analyze dataset biases; and more. However, their flexibility and generative capabilities also raise misuse concerns. This report discusses OpenAI's work related to the release of its GPT-2 language model. It discusses staged release, which allows time between model releases to conduct risk and benefit analyses as model sizes increased. It also discusses ongoing partnership-based research and provides recommendations for better coordination and responsible publication in AI.

Motivation & Objective

• Motivate examination of how language model releases can balance benefits and harms.
• Analyze GPT-2's staged release as a method for risk and benefit assessment as models scale.
• Propose coordination mechanisms and partnership-based research to improve responsible publication in AI.

Proposed method

• Describe staged release as a mechanism to monitor risks and benefits as model sizes increase.
• Discuss ongoing partnership-based research as a mode to share findings and lessons.
• Provide recommendations for better coordination and responsible publication in AI.

Experimental results

Research questions

• RQ1 What are the social and safety considerations that arise with releasing progressively larger language models?
• RQ2 How can staged release and partnership-based research improve risk-benefit analyses and responsible publication?
• RQ3 What recommendations can enhance coordination among researchers and institutions in AI releases?

Key findings

• The report analyzes OpenAI's GPT-2 release strategy as a case study for staged releases and risk assessment.
• It advocates ongoing partnership-based research to better understand social impacts and benefits of language models.
• The authors offer recommendations for improved coordination and responsible publication in AI.