Real, Fake, or Manipulated? Detecting Machine-Influenced Text (2025)

Abstract: Large Language Model (LLMs) can be used to write or modify documents, presenting a challenge for understanding the intent behind their use. For example, benign uses may involve using LLM on a human-written document to improve its grammar or to translate it into another language. However, a document entirely produced by a LLM may be more likely to be used to spread misinformation than simple translation (, from use by malicious actors or simply by hallucinating). Prior works in Machine Generated Text (MGT) detection mostly focus on simply identifying whether a document was human or machine written, ignoring these fine-grained uses. In this paper, we introduce a HiErarchical, length-RObust machine-influenced text detector (HERO), which learns to separate text samples of varying lengths from four primary types: human-written, machine-generated, machine-polished, and machine-translated. HERO accomplishes this by combining predictions from length-specialist models that have been trained with Subcategory Guidance. Specifically, for categories that are easily confused (, different source languages), our Subcategory Guidance module encourages separation of the fine-grained categories, boosting performance. Extensive experiments across five LLMs and six domains demonstrate the benefits of our HERO, outperforming the state-of-the-art by 2.5-3 mAP on average.

## introduction fine-grained machine generated text (fg-mgt) detection aims to predict if a document was humanwritten, machine-generated, or some combination thereof. prior work has primarily focused on separating paraphrased or machine-polished text figure 1 : paraphrasing/polishing human-written text or translating it into another language are often benign applications of an llm that users of mgt detectors, like moderators, may wish to ignore. however, off-the-shelf methods (e.g., @xcite ) often identify this type of data as machine-generated. in this paper, we increase the practical use of mgt detectors by separating text into fine-grained production categories, providing insight into content intent. from human and/or completely machine-generated text @xcite @xcite , as these are often benign uses of a language model. in contrast, machine generated text may hallucinate @xcite @xcite @xcite @xcite and is more likely to contain misinformation @xcite , making them less trustworthy. however, prior work ignores other benign uses of llms, like machine translation, which may also be flagged as machine-generated by traditional mgt detectors (see fig. 1 ). to address this issue, in this paper we introduce a hierarchical, length-robust machine-influenced text detector (hero), which provides fine-grained labels to better understand document authorship. specifically, as shown in fig. 2 , we expand the set of possible authorship categories to not only include machine translated text, but also the source language from which it is translated from. as we ## related work most prior work in detecting machine generated text (mgt) treats this task as a binary classification problem @xcite @xcite @xcite @xcite @xcite , i.e., detecting whether the input text is human-written or machine-generated. these include metric-based methods @xcite @xcite @xcite , which extract distinguishable features from the text using the target language models. e.g., @xcite apply log probability, @xcite use the absolute rank of each token, and @xcite searches over a language model's feature space. many of these methods (e.g., @xcite @xcite , rely on an observation that small changes to generated text typically lower its log probability under the language model, a pattern not machine generated/human ized (128 tokens) openai said on tuesday that it had begun training a new flagship… human written (256 tokens) openai also stated that it is establishing a new safety… translated (512 tokens) expert detector length 256 expert detector length 512 expert detector length 128 figure 3 : our hero framework. each input is processed by a specialized expert detector based on its token length. in addition to the standard cross-entropy loss, we introduce generated subcategory guidance to machine-generated and machine-humanized text, while translated subcategory guidance is used for translated text. see sec. 3 for discussion. some recent studies have recognized the importance of detecting other categories of mgt @xcite @xcite , including machine paraphrased and translated text. for example, @xcite al. (2024 ## expanding fine-grained machine generated text detection given an article x @xmath0, fine-grained machinegenerated text (fg-mgt) detection aims to separate samples into a set of categories y @xmath1, 1, . . . , k} where y @xmath2, and y @xmath3, . . . , k} corresponds to one of k distinct categories of machine-influenced text. prior work on fg-mgt explored up to four categories: human written, machine generated, humanized machine generated, and paraphrased/polished human written text @xcite @xcite . however, this ignores translated text, another form of machine-influenced generation with often benign use, but, as shown in fig. 1 , may be detected as llm-generated. thus, to provide additional insight for users of fg-mgt models, we add a new category based on the source language a document was translated from. however, as we will show, we find that separating these types of similar generation types is challenging, especially on out-of-domain generators used at test time. to address our fg-mgt task, we introduce hi-erarchical, length-robust machine-influenced text detector (hero), which makes two improvements to fg-mgt detectors. first, sec. 3.1.1 describes our subcategory guidance modules, which help construct a feature representation that can more easily separate similar categories. second, sec. 3.1.2 discusses our length-expert approach to improving support for varying document lengths. sec. 3.2 discusses our data generation process that we use to train and evaluate our fg-mgt detectors. ## conclusion in this paper, we conduct an in-depth study of fine-grained mgt detection, aiming to further distinguish between machine translated and machine paraphrased texts from mgt. we introduced hero, a fine-grained machine-influenced text detection framework that goes beyond the classical binary classification approach. our hierarchical structure, combined with length-specialist models, enables strong generalization across diverse llms and varying input lengths, making it suitable for real-world applications. our extensive experiments across multiple llms and different datasets show that hero consistently outperforms the state-ofthe-art by 2.5-3 map, and does especially well in out of domain settings. we also show that identifying source languages can boost a model's ability to identify translated text. overall, hero enables more accurate detection of machine-influenced content, which is essential for future works in discerning between benign and malicious uses of llms. acknowledgments. this material is based upon work supported by darpa under agreement number hr00112020054. any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the supporting agencies.