Evaluating C hat N et Z ero, an LLM -Chatbot to Demystify Climate Pledges (2024)

Abstract: This paper introduces and evaluates ChatNetZero, a large-language model (LLM) chatbot developed through Retrieval-Augmented Generation (RAG), which uses generative AI to produce answers grounded in verified, climate-domain specific information. We describe ChatNetZero’s design, particularly the innovation of anti-hallucination and reference modules designed to enhance the accuracy and credibility of generated responses. To evaluate ChatNetZero’s performance against other LLMs, including GPT-4, Gemini, Coral, and ChatClimate, we conduct two types of validation: comparing LLMs’ generated responses to original source documents to verify their factual accuracy, and employing an expert survey to evaluate the overall quality, accuracy and relevance of each response. We find that while ChatNetZero responses show higher factual accuracy when compared to original source data, experts surveyed prefer lengthier responses that provide more context. Our results highlight the importance of prioritizing information presentation in the design of domain-specific LLMs to ensure that scientific information is effectively communicated, especially as even expert audiences find it challenging to assess the credibility of AI-generated content.

## introduction in the era of generative ai, the proliferation of climate change misinformation presents a significant challenge, impeding both scientific discourse and efforts to distinguish between credible and noncredible climate actions. although scientific consensus has identified the imperative to achieve "netzero" emissions by mid-century, widespread disagreement over its definition and implementation remains @xcite . for example, according to the latest pew research center poll, two-thirds of americans say that the us should use a mix of energy sources, including fossil fuels, which are fundamentally incompatible with a netzero world @xcite . a surge of over 11,000 private and subnational entities have committed to respective decarbonization pledges, albeit with varying degrees of credibility @xcite . across the world, citizens and regulators are increasingly resorting to litigation to combat false and disingenuous net-zero claims @xcite . with more users relying on artificial intelligencedriven large language models (llms) like google's gemini @xcite and ope-nai's chatgpt @xcite to obtain primary information, it is a foregone conclusion that the public will turn to these resources to gain a deeper understanding of what governments and businesses are doing on climate change and decarbonization. these tools, however, are not attune to the rapidly evolving landscape of climate policy, specifically the nuances of net-zero goals, where definitions and interpretations of credibility are evolving daily. since non-state actors report climate actions in a variety of formats (e.g., press releases, pdf reports, spreadsheets, websites, etc.) @xcite , even the task of assembling a coherent dataset to analyze and compare entities' climate change strategies is challenging. generative ai is prone to "hallucination," where models produce seemingly real responses that could fail to correspond to any actual input, posing potential risks of hazardous and legally-disputable claims @xcite . here we introduce and evaluate chatnetzeroan llm-based chatbot developed through retrieval-augmented generation (rag)-which employs generative ai to produce answers to users' questions that are grounded in verified information @xcite . it is designed to analyze unstructured net-zero related text documents and serve as a question-answering platform for climate policy-specific information. chatnetzero is able to accurately answer questions relating to broad net-zero domain knowledge, such as different terminology used to articulate net-zero commitments, as well as specific details on an entity's net-zero pledge and its content. to evaluate chatnetzero's ability to provide accurate, high-quality responses, we assess its responses in two ways -comparing the generated responses to original expert texts; and engaging climate policy experts to evaluate its responses compared to other population chatbots, including gpt-4 (openai, 2023), gemini @xcite , and coral @xcite , as well as the climate-domain specific chatclimate @xcite . ## 84 chunking all source documents, including excel and pdf data, is converted into plain text, which is then segmented into chunks ranging from 50 to 1,000 words. to maintain the source data structure, each chunk encapsulates entire paragraphs. each chunk is then embedded into a large, high-dimensional numerical vector, which represents the meaning of the text @xcite . for traceability, every chunk is tagged with its originating document's name and page number, facilitating later checks against potential hallucination. embedding we embed both the chunked source documents and the user queries using openai's text-embedding-ada-002 model. this embedding space is used to perform semantic search between user queries and chunks from the source documents, allowing us to find the most relevant chunks to inform chatnetzero's final output response (see section 3.2.1). fate chose a small chunk size because when the response chunks were significantly longer than the user query, we found that the semantic search performed poorly, resulting in the selection of chunks that were not relevant to the user's question. ## discussion the design of chatnetzero and our comparison of its outputs to one climate domain-specific llm (chatclimate) and other popular llms @xcite 9. what does it mean for a companys net zero target to be 1 5c aligned? 10. is apple's net zero target credible? 11. what does it mean for an entity to contribute a fair share of emissions reductions? factual accuracy relevance fer broader context, even if the added information isn't always accurate or verifiable (tables 2, 3, 4). this "verbosity bias" @xcite indicates that humans tend to prefer longer, more detailed answers, believing they are more accurate than concise ones. similarly, @xcite found that users favor fluent, grammatical responses and sophisticated linguistic dialogue, even when these responses lack trustworthy information. we reviewed experts' qualitative comments to gain further insight. notably, experts who provided additional comments regarding the length of the evaluated llms' responses said they favored longer answers provided by gemini and gpt-4. this preference likely contributed to the higher scores for more extensive responses compared to the more concise bullet-point answers from chat-netzero, which were designed for brevity but were seen as disrupting the flow of information and reducing readability, despite the high accuracy of chatnetzero's responses compared to the lengthier responses of other llms like gemini and gpt-4. ## 89 we found that many responses from other llms contained auxiliary statements that were not factbased. while these statements enhance the answers' readability, they could potentially lead to misinterpretation about the validity or adequacy of the responses. for instance, in our earlier example about walmart and amazon (figure 3 : question 1), one llm stated, "as of 2023, walmart, amazon, and other large retail stores have been increasingly vocal and active in their commitments to sustainability and addressing climate change." this introductory statement, without specific evidence or references to their enhanced vocal and active roles, could potentially lead to issues like greenwashing if not carefully scrutinized. this result from our study highlights key lessons from the science communication literature, which emphasizes the importance of information presentation: scientific information should be comprehensible, aid understanding through layout and visualizations, and use appropriate sources and references @xcite . since we removed the reference features from all llms in our human evaluation study, users were not presented with this third criterion for presentational adequacy of scientific information-sources and citations-which might have influenced their evaluation of the overall quality or factual accuracy of the llm responses. as a result, users were neither able to individually verify the accuracy of the responses nor use this feature to gauge response quality. future validation efforts could involve asking users to assess llm responses in conjunction with the provided references and to evaluate the sources themselves. ## limitations our study here is not without its limitations. the number of experts responding to our study (around 10 in total) was relatively modest and mainly represented the academic, scientific and policy communities. in the future, we could expand user evaluation to other demographics, including business, government, activist, or non-expert audiences. second, by removing references from the llmgenerated responses, it's unclear how their inclusion by chatnetzero and gemini might have influenced experts' views or the perceived quality, accuracy, or relevance of the responses. last, different parameter tunings, for example an llm's temperature parameter, could result in a range of different responses @xcite , which may affect a user's interpretation. future evaluation could test the sensitivity of a user's evaluation to the temperature parameter on chatnetzero's responses. last, we acknowledge that chatnetzero still assumes a base-level understanding of climate change concepts, including net zero. future versions should consider whether additional user prompting or response framing should be presented to assess the user's baseline knowledge or prompt them to specify their level of understanding. ## conclusion this study underscores the critical role and potential of specialized large language models like chatnetzero in enhancing understanding and engagement in climate policy discourse. by demonstrating higher factual accuracy in handling complex climate-related content, chatnetzero shows promise as a valuable tool for disseminating reliable information. however, challenges such as ensuring the presentation of information and contextual depth of responses remain. future enhancements to chatnetzero will consider options allowing users to customize response length and style. adding more language could potentially compromise response quality, a challenge that will need further refinement. addressing these challenges will be crucial for maximizing the utility of llms, ensuring that a customized llm such as chatnet-zero provides transparency in its algorithmic processes to instill the trust and confidence required for any tool to impact decision making. 9 90