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【2024美赛】在COMAP比赛中使用大型语言模型和生成式AI工具的政策Use of Large Language ModelGenerative AI Tools in COMAP Contests
【2024美赛】A题（中英文）：资源可用性与性别比例Problem A: Resource Availability and Sex Ratios
【2024美赛】B题（中英文）：搜寻潜水器Problem B: Searching for Submersibles
【2024美赛】C题（中英文）：网球中的势头Problem C: Momentum in Tennis
【2024美赛】D题（中英文）：五大湖水资源问题Problem Problem D: Great Lakes Water Problem
【2024美赛】E题（中英文）：房产保险的可持续性Problem E: Sustainability of Property Insurance
【2024美赛】F题（中英文）：减少非法野生动物贸易Problem F: Reducing Illegal Wildlife Trade

中文翻译

在COMAP比赛中使用大型语言模型和生成式AI工具的政策

这项政策是由于大型语言模型（LLMs）和生成式AI辅助技术的兴起而提出的。该政策旨在为团队、顾问和评委提供更大的透明度和指导。这项政策适用于学生工作的所有方面，从研究和开发模型（包括代码创建）到书面报告。由于这些新兴技术正在迅速发展，COMAP将适时对这一政策进行完善。

团队必须对使用AI工具的所有情况开放和诚实。团队及其提交的内容越透明，其工作就越有可能被完全信任、赏识并被他人正确使用。这些披露有助于理解智力工作的发展和对贡献的适当认可。如果没有对AI工具的作用进行开放和明确的引用和参考文献，那么可疑的段落和工作更有可能被识别为抄袭并取消资格。

解决问题不需要使用AI工具，尽管允许负责任地使用它们。COMAP认识到LLMs和生成式AI作为生产力工具的价值，这些工具可以帮助团队准备他们的提交；例如，生成结构的初步想法，或在总结、改写、语言润色等方面。在模型开发的许多任务中，人类的创造力和团队合作是必不可少的，而依赖AI工具引入风险。因此，我们建议在使用这些技术进行模型选择和构建、辅助创建代码、解释模型的数据和结果以及得出科学结论等任务时谨慎行事。

重要的是要注意，LLMs和生成式AI有其局限性，无法取代人类的创造力和批判性思维。如果团队选择使用LLMs，COMAP建议团队注意这些风险：

• 客观性：之前发布的包含种族主义、性别歧视或其他偏见的内容可能出现在LLM生成的文本中，一些重要的观点可能不被代表。
• 准确性：LLMs可能会“幻想”即生成虚假内容，特别是在超出其领域或处理复杂或模棱两可的主题时。它们可以生成在语言上但非科学上合理的内容，它们可以搞错事实，并且已经显示出它们可以生成不存在的引用。一些LLMs只训练了特定日期之前发布的内容，因此呈现了一个不完整的画面。
• 上下文理解：LLMs不能将人类理解应用于文本的上下文，特别是在处理成语表达、讽刺、幽默或比喻性语言时。这可能导致生成内容中的错误或误解。
• 训练数据：LLMs需要大量高质量的训练数据才能达到最佳性能。然而，在某些领域或语言中，这样的数据可能不容易获得，从而限制了任何输出的有用性。

团队指南

团队需要：

1. 在报告中明确指出使用LLMs或其他AI工具，包括使用了哪个模型以及用途。请使用文中引用和参考文献部分。还需在你的25页解决方案之后附上AI使用报告（如下所述）。
2. 验证由语言模型生成的内容及任何引用的准确性、有效性和适当性，并纠正任何错误或不一致之处。
3. 提供引用和参考文献，按照这里提供的指导。仔细检查引用以确保它们准确且得到适当引用。
4. 意识到抄袭的潜在风险，因为LLMs可能会复制其他来源的大量文本。检查原始来源以确保你没有抄袭别人的工作。

当我们识别出可能是未声明使用此类工具准备的提交时，COMAP将采取适当行动。

引用和参考文献指南

仔细考虑如何记录和引用团队可能选择使用的任何工具。各种风格指南开始纳入AI工具引用和参考的政策。在你的25页解决方案的参考文献部分使用文中引用，并列出所有使用的AI工具。

无论团队是否选择使用AI工具，主要解决报告仍限制在25页。如果团队选择使用AI，在报告结束后，添加一个名为AI使用报告的新部分。这个新部分没有页数限制，不计入25页解决方案内。

示例（这不是详尽无遗的 - 根据你的情况调整这些示例）：

AI使用报告

1. OpenAI ChatGPT（2023年11月5日版本，ChatGPT-4）
   Query1: <输入你向AI工具输入的确切措辞>
   Output: <插入AI工具的完整输出>
2. OpenAI Ernie（2023年11月5日版本，Ernie 4.0）
   Query1: <输入你对AI工具的任何后续输入的确切措辞>
   Output: <插入第二次查询的完整输出>
3. Github CoPilot（2024年2月3日版本）
   Query1: <输入你向AI工具输入的确切措辞>
   Output: <插入AI工具的完整输出>
4. Google Bard（2024年2月2日版本）
   Query: <输入你的查询的确切措辞>
   Output: <插入AI工具的完整输出>

英文原文

Use of Large Language Models and Generative AI Tools in COMAP Contests

This policy is motivated by the rise of large language models (LLMs) and generative AI assisted technologies. The policy aims to provide greater transparency and guidance to teams, advisors, and judges. This policy applies to all aspects of student work, from research and development of models (including code creation) to the written report. Since these emerging technologies are quickly evolving, COMAP will refine this policy as appropriate.

Teams must be open and honest about all their uses of AI tools. The more transparent a team and its submission are, the more likely it is that their work can be fully trusted, appreciated, and correctly used by others. These disclosures aid in understanding the development of intellectual work and in the proper acknowledgement of contributions. Without open and clear citations and references of the role of AI tools, it is more likely that questionable passages and work could be identified as plagiarism and disqualified.

Solving the problems does not require the use of AI tools, although their responsible use is permitted. COMAP recognizes the value of LLMs and generative AI as productivity tools that can help teams in preparing their submission; to generate initial ideas for a structure, for example, or when summarizing, paraphrasing, language polishing etc. There are many tasks in model development where human creativity and teamwork is essential, and where a reliance on AI tools introduces risks. Therefore, we advise caution when using these technologies for tasks such as model selection and building, assisting in the creation of code, interpreting data and results of models, and drawing scientific conclusions.

It is important to note that LLMs and generative AI have limitations and are unable to replace human creativity and critical thinking. COMAP advises teams to be aware of these risks if they choose to use LLMs:

• Objectivity: Previously published content containing racist, sexist, or other biases can arise in LLM-generated text, and some important viewpoints may not be represented.
• Accuracy: LLMs can ‘hallucinate’ i.e. generate false content, especially when used outside of their domain or when dealing with complex or ambiguous topics. They can generate content that is linguistically but not scientifically plausible, they can get facts wrong, and they have been shown to generate citations that don’t exist. Some LLMs are only trained on content published before a particular date and therefore present an incomplete picture.
• Contextual understanding: LLMs cannot apply human understanding to the context of a piece of text, especially when dealing with idiomatic expressions, sarcasm, humor, or metaphorical language. This can lead to errors or misinterpretations in the generated content.
• Training data: LLMs require a large amount of high-quality training data to achieve optimal performance. In some domains or languages, however, such data may not be readily available, thus limiting the usefulness of any output.

Guidance for teams

Teams are required to:

1. Clearly indicate the use of LLMs or other AI tools in their report, including which model was used and for what purpose. Please use inline citations and the reference section. Also append the Report on Use of AI (described below) after your 25-page solution.
2. Verify the accuracy, validity, and appropriateness of the content and any citations generated by language models and correct any errors or inconsistencies.
3. Provide citation and references, following guidance provided here. Double-check citations to ensure they are accurate and are properly referenced.
4. Be conscious of the potential for plagiarism since LLMs may reproduce substantial text from other sources. Check the original sources to be sure you are not plagiarizing someone else’s work.

COMAP will take appropriate action when we identify submissions likely prepared withundisclosed use of such tools.

Citation and Referencing Directions

Think carefully about how to document and reference whatever tools the team may choose to use. A variety of style guides are beginning to incorporate policies for the citation and referencing of AI tools. Use inline citations and list all AI tools used in the reference section of your 25-page solution.

Whether or not a team chooses to use AI tools, the main solution report is still limited to 25 pages. If a team chooses to utilize AI, following the end of your report, add a new section titled Report on Use of AI. This new section has no page limit and will not be counted as part of the 25-page solution.

Examples (this is not exhaustive – adapt these examples to your situation):

Report on Use of AI

1. OpenAI ChatGPT (Nov 5, 2023 version, ChatGPT-4)
   Query1: <insert the exact wording you input into the AI tool>
   Output: <insert the complete output from the AI tool>
2. OpenAI Ernie (Nov 5, 2023 version, Ernie 4.0)
   Query1: <insert the exact wording of any subsequent input into the AI tool>
   Output: <insert the complete output from the second query>
3. Github CoPilot (Feb 3, 2024 version)
   Query1: <insert the exact wording you input into the AI tool>
   Output: <insert the complete output from the AI tool>
4. Google Bard (Feb 2, 2024 version)
   Query: <insert the exact wording of your query>
   Output: <insert the complete output from the AI tool>

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