We demonstrate a proof-of-concept of a large language model conducting corporate lobbying related activities. We use an autoregressive large language model (OpenAI's text-davinci-003) to determine if proposed U.S. Congressional bills are relevant to specific public companies and provide explanations and confidence levels. For the bills the model deems as relevant, the model drafts a letter to the sponsor of the bill in an attempt to persuade the congressperson to make changes to the proposed legislation. We use hundreds of ground-truth labels of the relevance of a bill to a company to benchmark the performance of the model, which outperforms the baseline of predicting the most common outcome of irrelevance. However, we test the ability to determine the relevance of a bill with the previous OpenAI GPT-3 model (text-davinci-002), which was state-of-the-art on many language tasks until text-davinci-003 was released on November 28, 2022. The performance of text-davinci-002 is worse than simply always predicting that a bill is irrelevant to a company. These results suggest that, as large language models continue to improve core natural language understanding capabilities, performance on corporate lobbying related tasks will continue to improve. We then discuss why this could be problematic for societal-AI alignment.
翻译:我们用一个自动递减的大型语言模式(OpenAI's text-davinci-003)来确定拟议的美国国会法案是否与特定公营公司相关,并提供解释和信任程度。对于该模式认为相关的法案,该模式草拟了一封信给该法案的发起人,以试图说服议员修改拟议的立法。我们使用数百个关于法案与公司基准测试该模式绩效的地面真实标签,该模式比预测最常见无关性结果的基线要强。然而,我们测试了与先前的OpenAI GPT-3模式(text-davinci-002)确定法案相关性的能力,该模式在2022年11月28日发布文本-davinc-003之前是许多语言任务的最新版本。文本-Davinci-002的绩效比一直预测一项法案与公司无关的情况还要差。这些结果表明,我们能否确定法案与先前的OpenAI GPT-3模式(t-Davinici-002) (t) (text) (tle-davinci-003) (t) (tal-legal-fulnationality commissional commissional commissional lading) exulational ex ex ex exulationalationality exulationality exulationalbulation exulationalbololationality ex ex exulationality exulationality exbolence sabolence sabolfulity sacility sacil) sabulding sabulity sacil sacil 会继续讨论如何改进。我们讨论与大的核心能力。我们如何才能。我们讨论了与大的核心。我们讨论了。我们讨论了。我们讨论了如何。我们讨论了如何。我们讨论如何。我们继续讨论如何。