Should results found using large language model technology be written into a preprint?
Suppose that a layperson or student discovers a new mathematical result using ChatGPT and Aristotle (https://aristotle.harmonic.fun/). They carefully verify that the proof is correct, and it ends up being, say, 20 to 100 pages long. Would it be worth sending the work to a professor, posting it on arXiv, or submitting it to a journal? Or would it be automatically rejected if it lacks a proper literature review or discussion of ethical issues?
1 Answer
Writing a preprint of your new mathematical result can be a great way to share your discovery with the academic community, but it's essential to consider the context and potential implications. Before deciding on a course of action, take some time to reflect on your goals. Are you seeking feedback from peers, or do you want to publish your work in a formal journal? If you're looking for feedback, posting it on arXiv or a similar platform might be a good option. ArXiv is a well-established repository for preprints in various fields, including mathematics, and it can help you get your work seen by experts in the field. However, if you're aiming to publish your work in a formal journal, you'll need to ensure that it meets the journal's standards. This might involve revising your work, adding a literature review, and discussing potential ethical implications. Journals often have specific requirements for preprints, so it's crucial to review their guidelines before submitting. Another approach is to discuss your work with a professor or mentor, who can provide guidance on the best way to proceed. They may be able to offer valuable feedback, help you refine your work, or connect you with the right people in the field. Ultimately, the decision to write a preprint depends on your goals and the level of scrutiny you're comfortable with. By considering your options carefully, you can make an informed decision that suits your needs. Writing a preprint is a great way to share your discovery with the academic community and get feedback from peers.
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