prompter: How can a system potentially recognise an input (say text, image or other media) to be factual and or original , derivative to a non trivial degree or plainly plagiaristic in nature. Would such a system be stumbling into a Godel halting problem scenario ?
assistant: Recognizing whether an input is factual, original, derivative, or plagiaristic is a complex task that involves multiple factors and can be challenging for a system. However, there are several techniques and approaches that can be used to assess the originality and authenticity of an input.

For textual input, one common approach is to use natural language processing (NLP) techniques such as language modeling, topic modeling, and sentiment analysis to analyze the text and identify patterns that indicate plagiarism or originality. Another approach is to use text similarity metrics such as cosine similarity or Levenshtein distance to compare the input to other texts and identify potential sources of plagiarism.

For image and other media inputs, techniques such as image recognition, reverse image search, and metadata analysis can be used to assess the originality and authenticity of the content.

Regarding the Godel halting problem scenario, it is not necessarily applicable in this context. The Godel halting problem refers to the challenge of determining whether a given computer program will halt (i.e., stop running) or run indefinitely. While plagiarism detection and authenticity assessment systems can encounter issues related to accuracy and false positives, they do not involve the same type of fundamental computational challenge as the halting problem.
prompter: How can I prevent my students from using large language models to cheat on assignments?
assistant: Here are some suggestions for preventing students from using large language models to cheat on assignments:

• Require structured, focused writing rather than open-ended essays. Assignments with specific prompts, requirements, and formatting constraints are harder to autocomplete using language models. Things like outlines, summaries, code snippets, etc. are better than long-form essays.

• Require significant in-depth analysis or technical content beyond just summarizing information. Large language models are good at rephrasing and summarizing information, but struggle with analysis or complex, specialized insights and knowledge. Focus on assignments that require that deeper kind of work.

• Include explicit plagiarism detection as part of the grading process. Some tools can detect use of language models in student writing. While not foolproof, using a plagiarism checker can at least hold students accountable if they do use language models. You can explain that use of such models would be considered a type of plagiarism or academic dishonesty.

• Closely analyze a subset of submissions to check for language model use. If you have teaching assistants helping with grading, have them focus in on certain submissions to scrutinize for signs of language model use like repetitive or nonsensical phrases, unusual word choices, or lack of depth. This spot-checking can at least raise the risk and discourage some students from attempting to cheat in this way.

• Be transparent that you are aware of potential language model use and will be checking for it. Even just communicating to students that you are aware of this possibility and will be rigorously reviewing submissions for authentic work may deter some students from attempting to use the models. Making the consequences of getting caught clear can increase the deterrence.