What can AI Language Models tell us about how textual information influences understanding of environmental issues?

为了应对和减轻气候变化潜在的灾难性影响，人类需要对自己的行为做出重大改变。这种变化需要广泛了解个人和集体的人类活动如何影响环境，并在此基础上采取实际行动。实现这种理解和行动不仅取决于相关信息的传播，而且还取决于它的同化，更重要的是，它引起的激励反应。因此，研究如何呈现信息并分析不同形式的信息可能产生的影响，对于促进应对气候变化和环境退化的有效行动至关重要。人工智能研究人员在过去十年中开发的基于神经网络的语言模型(例如Elmo，Bert，以及最近的GPT3，Chat GPT)能够学习和复制自然语言文本的典型模式，在某种程度上，给定一些自然语言文本输入作为起点，语言模型可以生成似乎非常可信的输入的延续，并类似于人类在给定提示时可能做出的阐述或反应的类型。例如，这一功能可用于为聊天机器人、网页创建和碳追踪智能手机应用程序中的用户界面等应用程序生成文本。这样的模型还可以用来对不同潜在延续的可能性进行量化和排序。这可以支持从文本派生解释或推断的功能。例如，可以对一组可能的响应进行排序，以便找到由给定提示最强烈地“暗示”的一个。或者相反，如果我们想要引起某些特定的响应，我们可以搜索将给予所需继续的高可能性的提示文本。大量的应用程序表明，这种功能可以支持复杂和有用的应用程序。然而，语言模型的优势和劣势仍然鲜为人知，信息提示的可选延续的数字排名也没有明确的语义。因此，这个博士项目的重点将是调查人工智能语言模型的稳健性和可靠性，目的是找到有效的方法来告知和激励人类与气候变化有关的问题。由于该项目将调查人工智能语言模型方法的局限性，我们预计它可能会考虑其他方法来解释人类如何解释和回应信息。该项目可能会考虑这些替代视角中的一个或多个如何在人工智能语言模型方面补充分析，并可能探索结合不同方法的方法。作为博士工作的一部分，预计学生将使用并帮助开发由Bennett博士在首字母缩写KARaML(使用推理和机器学习的知识同化)框架内组装的软件工具集合。该工具集提供了语言模型的接口，以及支持语义分析(K-Parser)和逻辑推理(Clasp、Prover9、吸血鬼)的工具。使用该软件需要具备编程能力(特别是使用Python语言)。

In order to address and mitigate the potentially catastrophic impact of climate change, humans will need to make drastic changes to their behaviour. Such changes will require widespread understanding of how individual and collective human activities affect the environment, as well as practical action based on this understanding. Achieving such understanding and action will depend not only on dissemination of relevant information but also its assimilation, and critically, the motivational response that it evokes. Thus, investigation of how to present information and analysis of the influence different forms of information may have, is vital to promoting effective action in response to climate change and environmental degradation.The neural net based Language Models developed by Artificial Intelligence researchers over the last decade (for example ELMo, BERT and, more recently, GPT3, Chat GPT) are able to learn and replicate typical patterns of natural language text to the extent that, given some natural language text input as a starting point, a language model can generate a continuation of the input that seems highly plausible and similar to the kind of elaboration or response that a human might make given that prompt. This functionality can be used to generate text for applications such as chatbots, web page creation and user interfaces in carbon tracking smartphone apps for instance. Such models can also be used to quantify and rank the likelihood of different potential continuations. This can support functionality that derives interpretations or inferences from text. For example, one can rank a set of possible responses in order to find the one most strongly "implied" by a given prompt. Or conversely, if we would like to elicit some particular response, we could search for prompting text that would impart a high likelihood to the desired continuation.Numerous applications show that this kind of functionality can support sophisticated and useful applications. However, the strengths and weaknesses of language models are still poorly understood, and the numerical ranking of alternative continuations of an information prompt does not have a clear semantics. Hence, the focus of this PhD project will be to investigate robustness and reliability of AI language models, with the aim of finding effective ways of informing and motivating humans in relation to climate change issues.Since the project will investigate limitations of AI Language Model approaches, we expect that it may consider other approaches to explaining how humans interpret and respond to information. The project may consider how one or more of these alternative perspectives may complement the analysis in terms of AI Language Models, and could potentially explore ways of combining different approaches. As part of the PhD work, it is envisaged that the student will use and contribute to the development of a collection of software tools assembled by Dr Bennett within a framework called by the acronym KARaML (Knowledge Assimilation using Reasoning and Machine Learning). This tool set provides interfaces to both Language Models as well as tools supporting semantic analysis (K-Parser) and logical reasoning (Clasp, Prover9, Vampire). The use of this software will require competency in programming (in particular using the Python language).