Creating a Cooperative AI Policymaking Platform through Open Source Collaboration
Authors:
Aiden Lewington,
Alekhya Vittalam,
Anshumaan Singh,
Anuja Uppuluri,
Arjun Ashok,
Ashrith Mandayam Athmaram,
Austin Milt,
Benjamin Smith,
Charlie Weinberger,
Chatanya Sarin,
Christoph Bergmeir,
Cliff Chang,
Daivik Patel,
Daniel Li,
David Bell,
Defu Cao,
Donghwa Shin,
Edward Kang,
Edwin Zhang,
Enhui Li,
Felix Chen,
Gabe Smithline,
Haipeng Chen,
Henry Gasztowtt,
Hoon Shin
, et al. (26 additional authors not shown)
Abstract:
Advances in artificial intelligence (AI) present significant risks and opportunities, requiring improved governance to mitigate societal harms and promote equitable benefits. Current incentive structures and regulatory delays may hinder responsible AI development and deployment, particularly in light of the transformative potential of large language models (LLMs). To address these challenges, we p…
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Advances in artificial intelligence (AI) present significant risks and opportunities, requiring improved governance to mitigate societal harms and promote equitable benefits. Current incentive structures and regulatory delays may hinder responsible AI development and deployment, particularly in light of the transformative potential of large language models (LLMs). To address these challenges, we propose developing the following three contributions: (1) a large multimodal text and economic-timeseries foundation model that integrates economic and natural language policy data for enhanced forecasting and decision-making, (2) algorithmic mechanisms for eliciting diverse and representative perspectives, enabling the creation of data-driven public policy recommendations, and (3) an AI-driven web platform for supporting transparent, inclusive, and data-driven policymaking.
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Submitted 9 December, 2024;
originally announced December 2024.
DataAgent: Evaluating Large Language Models' Ability to Answer Zero-Shot, Natural Language Queries
Authors:
Manit Mishra,
Abderrahman Braham,
Charles Marsom,
Bryan Chung,
Gavin Griffin,
Dakshesh Sidnerlikar,
Chatanya Sarin,
Arjun Rajaram
Abstract:
Conventional processes for analyzing datasets and extracting meaningful information are often time-consuming and laborious. Previous work has identified manual, repetitive coding and data collection as major obstacles that hinder data scientists from undertaking more nuanced labor and high-level projects. To combat this, we evaluated OpenAI's GPT-3.5 as a "Language Data Scientist" (LDS) that can e…
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Conventional processes for analyzing datasets and extracting meaningful information are often time-consuming and laborious. Previous work has identified manual, repetitive coding and data collection as major obstacles that hinder data scientists from undertaking more nuanced labor and high-level projects. To combat this, we evaluated OpenAI's GPT-3.5 as a "Language Data Scientist" (LDS) that can extrapolate key findings, including correlations and basic information, from a given dataset. The model was tested on a diverse set of benchmark datasets to evaluate its performance across multiple standards, including data science code-generation based tasks involving libraries such as NumPy, Pandas, Scikit-Learn, and TensorFlow, and was broadly successful in correctly answering a given data science query related to the benchmark dataset. The LDS used various novel prompt engineering techniques to effectively answer a given question, including Chain-of-Thought reinforcement and SayCan prompt engineering. Our findings demonstrate great potential for leveraging Large Language Models for low-level, zero-shot data analysis.
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Submitted 29 March, 2024;
originally announced April 2024.