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.
这一政策的动机是大型语言模型(LLMs)和生成AI辅助技术的兴起。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各个方面，从模型的研究和开发(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将适当地完善这一策略。
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.
团队必须公开和诚实地使用AI工具。一个团队及其提交的内容越透明，他们的工作就越有可能得到他人的充分信任、赞赏和正确使用。这些披露有助于理解智力工作的发展和对贡献的适当承认。如果没有对AI工具作用的公开和清晰的引用和参考，那么有问题的段落和工作更有可能被认定为抄袭并被取消资格。
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.
解决这些问题不需要使用AI工具，尽管允许负责任地使用它们。COMAP认识到LLMs和生成AI作为生产力工具的价值，可以帮助团队准备提交;例如，为一个结构产生初步的想法，或者在总结、释义、语言润色等时。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，对AI工具的依赖会带来风险。因此，我们建议在将这些技术用于模型选择和构建、协助创建代码、解释模型的数据和结果以及得出科学结论等任务时要谨慎。
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:
值得注意的是，LLMs和生成式AI有局限性，无法取代人类的创造力和批判性思维。COMAP建议团队在选择使用LLMs时要意识到这些风险:
- Objectivity: Previously published content containing racist, sexist, or other biases canarise in LLM-generated text, and some important viewpoints may not be represented.
- 客观性: LLMs生成的文本中可能出现先前发表的包含种族主义、性别歧视或其他偏见的内容，一些重要观点可能未被代表
- Accuracy: LLMs can ‘hallucinate’ i.e. generate false content, especially when used outsideof their domain or when dealing with complex or ambiguous topics. They can generatecontent 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 onlytrained on content published before a particular date and therefore present anincomplete picture.
- Accuracy: LLMs可以产生“幻觉”，即产生虚假内容，特别是在他们的领域之外使用或处理复杂或模棱两可的主题时。他们可以生成语言上但科学上不合理的内容，他们可以错误地获取事实，并且他们已经被证明可以生成不存在的引用。一些法学硕士只接受特定日期之前发布的内容的培训，因此呈现的是不完整的画面。
- 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.
- 语境理解: LLMs不能将人类的理解应用到一篇文章的语境中，特别是在处理习惯用语、讽刺、幽默或隐喻语言时。这可能会导致生成的内容出现错误或误解。
- 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.
- Trainingdata: LLMs需要大量高质量的训练数据来达到最优性能。然而，在某些领域或语言中，这样的数据可能并不容易获得，从而限制了任何输出的有用性。
Guidance for teams
对团队的指导
Teams are required to:
参赛队伍需要:
- 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.
- Verify the accuracy, validity, and appropriateness of the content and any citations generated by language models and correct any errors or inconsistencies.
- Provide citation and references, following guidance provided here. Double-check citations to ensure they are accurate and are properly referenced.
- 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.
1. 在报告中明确指出使用了法学硕士或其他AI工具，包括使用了哪个模型以及用于什么目的。请使用内联引文和参考文献部分。在你的25页解决方案之后，还要附上AI使用报告(如下所述)
2. 验证内容的准确性、有效性和适当性以及由语言模型生成的任何引用，并纠正任何错误或不一致之处。
3. 提供引用和参考文献，遵循这里提供的指导。仔细检查引文，以确保它们是准确的，并被正确引用。
4. 要注意抄袭的可能性，因为LLMs 可能会从其他来源复制大量文本。检查原始来源，以确保你没有抄袭别人的作品。
COMAP will take appropriate action when we identify submissions likely prepared with
undisclosed use of such tools.
当COMAP发现可能使用未公开的工具准备的提交文件时，我们将采取适当的行动。
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.
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引用和参考AI工具的政策。在你的25页解决方案的参考部分，使用内联引用并列出所有使用的AI工具。
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 the25-page solution.
无论团队是否选择使用AI工具，主要解决方案报告仍然限制在25页。如果一个团队选择使用AI，在你的报告结束后，添加一个名为人工智能使用报告的新部分。这个新章节没有页数限制，不会被计入25页的解决方案中。
Examples (this is not exhaustive – adapt these examples to your situation):
例子(这不是详尽的-根据你的情况调整这些例子):
原始文件