来自今天的爱可可AI前沿推介

[CL] Large Language Models as Corporate Lobbyists

J J. Nay
[Stanford University]

大型语言模型作为企业说客

要点:

  1. 大型语言模型(特别是OpenAI的text-davinci-003)可用于开展公司游说活动，方法是确定拟议的国会法案与特定公司的相关性，并起草信件以说服国会议员修改立法；
  2. Text-davinci-003在公司游说任务上的性能优于无关的基线预测，且明显优于之前最先进的模型(text-davinci-002)；
  3. 随着大型语言模型的不断改进，其在企业游说任务方面的表现也可能有所改善；
  4. AI在游说中的使用，可能是AI影响法律的第一步，如果AI开始影响法律本身，可能会威胁到法律作为信息在使AI与人类保持一致方面所扮演的关键作用。

摘要：
本文展示了用大型语言模型进行公司游说相关活动的概念验证。用自回归大型语言模型(OpenAI的text-davinci-003)来确定拟议的美国国会法案是否与特定的上市公司相关，并提供解释和置信水平。对于模型认为相关的法案，该模型起草了一封给法案提案人的信，试图说服国会议员对拟议的立法进行修改。本文用数百个法案与公司相关性的真相标签来衡量该模型的性能，其表现优于预测最常见无关结果的基线。本文测试了确定法案与之前的OpenAI GPT-3模型(text-davinci-002)的相关性的能力，该模型在许多语言任务上都是最先进的，直到2022年11月28日text-davinci-003发布。Text-davinci-002的表现比总是简单地预测法案与公司无关更糟糕。这些结果表明，随着大型语言模型继续提高核心自然语言理解能力，企业游说相关任务的表现将继续提高。本文讨论了为什么这对社会-人工智能的一致性可能存在问题。

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.

论文链接：https://arxiv.org/abs/2301.01181
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