On the Detectability of LLM-Generated Text: What Exactly Is LLM-Generated Text?

2025-10-23

AI Summary

This paper addresses the fundamental challenge in LLM-generated text detection: the absence of a rigorous, universally accepted definition of “LLM-generated text,” compounded by heterogeneous models, diverse application contexts, human editing, and blurred human-AI collaboration boundaries—rendering the actual detection target an incomplete subset of raw LLM outputs. Adopting conceptual analysis and critical evaluation, the study deconstructs idealized assumptions underlying mainstream benchmarks and evaluation paradigms, exposing severe real-world biases arising from their neglect of editing provenance, prompt engineering, and model evolution. Key contributions are: (1) a redefined detection framework emphasizing contextual sensitivity and process traceability; (2) delineation of the effective operational boundaries of detection techniques; and (3) a methodological principle positioning detection outputs as auxiliary references—not definitive determinations—to support trustworthy AI content governance.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Search and Optimization: Evaluation and Analysis

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

With the widespread use of large language models (LLMs), many researchers have turned their attention to detecting text generated by them. However, there is no consistent or precise definition of their target, namely"LLM-generated text". Differences in usage scenarios and the diversity of LLMs further increase the difficulty of detection. What is commonly regarded as the detecting target usually represents only a subset of the text that LLMs can potentially produce. Human edits to LLM outputs, together with the subtle influences that LLMs exert on their users, are blurring the line between LLM-generated and human-written text. Existing benchmarks and evaluation approaches do not adequately address the various conditions in real-world detector applications. Hence, the numerical results of detectors are often misunderstood, and their significance is diminishing. Therefore, detectors remain useful under specific conditions, but their results should be interpreted only as references rather than decisive indicators.

Problem

• Lack of consistent definition for LLM-generated text
• Difficulty detecting edited or influenced LLM outputs
• Inadequate evaluation of real-world detector conditions

Innovation

• Proposes redefining LLM-generated text detection
• Addresses human edits and LLM influences
• Recommends interpreting detector results as references