Case Study: Machine-Generated Writing
Dr. Tanaka was assigned as a reviewer for a paper submitted to an ACM publication. As she began reading the paper, she noticed some things about the writing that gave her pause, such as that key experimental details were missing from the Methods and the same phrases were used in several places throughout the text, among others. The figures also had some irregularities.
Dr. Tanaka suspected that the author had used an LLM to generate the text, and even the figures, of the paper. She suspected that this was a violation of ACM policy and went about consulting the ACM website for more information.
Upon reading through the ACM Peer Review Policy Frequently Asked Questions and the ACM Policy on Authorship Frequently Asked Questions, Dr. Tanaka contacted the AE who had assigned her as reviewer and notified him of what she had discovered. The AE thanked her for the information and pursued an investigation of the situation. It was later confirmed that the author had, in fact, used an LLM for generating parts of the paper, including several of the figures, without disclosing this use, as is required by the ACM Policy. The paper was rejected, and the author received a penalty commensurate with the violation.
Module 3
►Review Touchstones
General Review Criteria
Relevance
Significance
Soundness
Clarity
Reproducibility
Research Integrity
Content Specific Ethical Issues
Data or Figure Manipulation
Machine-Generated Writing
CASE STUDY: Machine-Generated Writing
Module 4: Evaluating the Paper
Module 5: Submitting Your Review
Module 6: Artifact Review and Badging
ACM Peer Reviewer Certification Exam
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