Large language models (LLMs) have significantly transformed the landscape of artificial intelligence by demonstrating their ability in generating human-like text across diverse topics. However, despite their impressive capabilities, LLMs lack recent information and often employ imprecise language, which can be detrimental in domains where accuracy is crucial, such as climate change. In this study, we make use of recent ideas to harness the potential of LLMs by viewing them as agents that access multiple sources, including databases containing recent and precise information about organizations, institutions, and companies. We demonstrate the effectiveness of our method through a prototype agent that retrieves emission data from ClimateWatch (https://www.climatewatchdata.org/) and leverages general Google search. By integrating these resources with LLMs, our approach overcomes the limitations associated with imprecise language and delivers more reliable and accurate information in the critical domain of climate change. This work paves the way for future advancements in LLMs and their application in domains where precision is of paramount importance.

翻译：大型语言模型（LLMs）通过展示其在生成涵盖多样主题的类人文本方面的能力，显著改变了人工智能的格局。然而，尽管具有令人印象深刻的能力，LLMs缺乏最新信息，且常常使用不精确的语言，这在气候变化等准确性至关重要的领域中可能造成不利影响。在本研究中，我们利用最新思路，将LLMs视为能够访问多个来源的智能体，这些来源包括包含组织、机构及企业最新且精确信息的数据库。我们通过一个原型智能体证明了我们方法的有效性，该智能体从ClimateWatch（https://www.climatewatchdata.org/）检索排放数据，并利用通用谷歌搜索。通过将这些资源与LLMs整合，我们的方法克服了与不精确语言相关的局限性，并在气候变化这一关键领域提供了更可靠和准确的信息。这项工作为LLMs的未来进展及其在精度至上的领域中的应用铺平了道路。