论文标题
代码合成的危险分析框架大语言模型
A Hazard Analysis Framework for Code Synthesis Large Language Models
论文作者
论文摘要
Codex是一种在各种代码库中训练的大型语言模型（LLM），它以合成和生成代码的能力超过了先前的最新技术。尽管Codex提供了很多好处，但可能会以这种规模生成代码的模型具有重大的局限性，一致性问题，滥用的潜力以及增加技术领域的进度速度的可能性，这些进度率本身可能会产生不稳定的影响或滥用潜力。然而，这种安全影响尚未知道或尚待探索。在本文中，我们概述了在OpenAI构建的危险分析框架，以发现危害或安全风险，即诸如Codex之类的模型可能在技术，社会，政治和经济上强加于危险或安全风险。分析是由一个新颖的评估框架告知的，该框架确定了高级代码生成技术对规范提示的复杂性和表达性的能力，以及它们相对于人类能力的理解和执行它们的能力。
Codex, a large language model (LLM) trained on a variety of codebases, exceeds the previous state of the art in its capacity to synthesize and generate code. Although Codex provides a plethora of benefits, models that may generate code on such scale have significant limitations, alignment problems, the potential to be misused, and the possibility to increase the rate of progress in technical fields that may themselves have destabilizing impacts or have misuse potential. Yet such safety impacts are not yet known or remain to be explored. In this paper, we outline a hazard analysis framework constructed at OpenAI to uncover hazards or safety risks that the deployment of models like Codex may impose technically, socially, politically, and economically. The analysis is informed by a novel evaluation framework that determines the capacity of advanced code generation techniques against the complexity and expressivity of specification prompts, and their capability to understand and execute them relative to human ability.
