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Against the misuse (e.g., plagiarism or spreading misinformation) of LargeLanguage Models (LLMs), many recent works have presented LLM-generated-textdetectors with promising detection performance. Spotlighting a situation whereusers instruct LLMs to generate texts (e.g., essay writing), there are variousways to write the instruction (e.g., what task-oriented constraint to include).In this paper, we discover that even a task-oriented constraint in instructioncan cause the inconsistent performance of current detectors to the generatedtexts. Specifically, we focus on student essay writing as a realistic domainand manually create the task-oriented constraint for each factor on essayquality by Ke and Ng (2019). Our experiment shows that the detectionperformance variance of the current detector on texts generated by instructionwith each task-oriented constraint is up to 20 times larger than the variancecaused by generating texts multiple times and paraphrasing the instruction. Ourfinding calls for further research on developing robust detectors that candetect such distributional shifts caused by a task-oriented constraint in theinstruction.

How You Prompt Matters! Even Task-Oriented Constraints in Instructions Affect LLM-Generated Text Detection