DetectAnyLLM: Towards Generalizable and Robust Detection of Machine-Generated Text Across Domains and Models

• URL: http://arxiv.org/abs/2509.14268v1
• Date: Mon, 15 Sep 2025 10:59:57 GMT
• Title: DetectAnyLLM: Towards Generalizable and Robust Detection of Machine-Generated Text Across Domains and Models
• Authors: Jiachen Fu, Chun-Le Guo, Chongyi Li,
• Abstract summary: We propose Direct Discrepancy Learning (DDL) to optimize the detector with task-oriented knowledge.<n>Built upon this, we introduce DetectAnyLLM, a unified detection framework that achieves state-of-the-art MGTD performance.<n>MIRAGE samples human-written texts from 10 corpora across 5 text-domains, which are then re-generated or revised using 17 cutting-edge LLMs.
• Score: 60.713908578319256
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid advancement of large language models (LLMs) has drawn urgent attention to the task of machine-generated text detection (MGTD). However, existing approaches struggle in complex real-world scenarios: zero-shot detectors rely heavily on scoring model's output distribution while training-based detectors are often constrained by overfitting to the training data, limiting generalization. We found that the performance bottleneck of training-based detectors stems from the misalignment between training objective and task needs. To address this, we propose Direct Discrepancy Learning (DDL), a novel optimization strategy that directly optimizes the detector with task-oriented knowledge. DDL enables the detector to better capture the core semantics of the detection task, thereby enhancing both robustness and generalization. Built upon this, we introduce DetectAnyLLM, a unified detection framework that achieves state-of-the-art MGTD performance across diverse LLMs. To ensure a reliable evaluation, we construct MIRAGE, the most diverse multi-task MGTD benchmark. MIRAGE samples human-written texts from 10 corpora across 5 text-domains, which are then re-generated or revised using 17 cutting-edge LLMs, covering a wide spectrum of proprietary models and textual styles. Extensive experiments on MIRAGE reveal the limitations of existing methods in complex environment. In contrast, DetectAnyLLM consistently outperforms them, achieving over a 70% performance improvement under the same training data and base scoring model, underscoring the effectiveness of our DDL. Project page: {https://fjc2005.github.io/detectanyllm}.

Related papers

• Zero-shot HOI Detection with MLLM-based Detector-agnostic Interaction Recognition [71.5328300638085]
  Zero-shot Human-object interaction (HOI) detection aims to locate humans and objects in images and recognize their interactions.<n>Existing methods, including two-stage methods, tightly couple interaction recognition with a specific detector.<n>We propose a decoupled framework that separates object detection from IR and leverages multi-modal large language models (MLLMs) for zero-shot IR.
  arXiv  Detail & Related papers  (2026-02-16T19:01:31Z)
• Connecting the Dots: Training-Free Visual Grounding via Agentic Reasoning [63.109585527799005]
  GroundingAgent is a visual grounding framework that operates without task-specific fine-tuning.<n>It achieves an average zero-shot grounding accuracy of 65.1 % on widely-used benchmarks.<n>It also offers strong interpretability, transparently illustrating each reasoning step.
  arXiv  Detail & Related papers  (2025-11-24T03:11:08Z)
• Text-ADBench: Text Anomaly Detection Benchmark based on LLMs Embedding [27.02879006439693]
  This work performs a comprehensive empirical study and introduces a benchmark for text anomaly detection.<n>Our work systematically evaluates the effectiveness of embedding-based text anomaly detection.<n>By open-sourcing our benchmark toolkit, this work provides a foundation for future research in robust and scalable text anomaly detection systems.
  arXiv  Detail & Related papers  (2025-07-16T14:47:41Z)
• Retrieval Augmented Generation based Large Language Models for Causality Mining [3.711112570761809]
  Causality detection and mining are important tasks in information retrieval.<n>We present several retrieval-augmented generation (RAG) based dynamic prompting schemes to enhance causality detection and extraction tasks.
  arXiv  Detail & Related papers  (2025-05-29T18:51:00Z)
• Optimizing Multispectral Object Detection: A Bag of Tricks and Comprehensive Benchmarks [49.84182981950623]
  Multispectral object detection, utilizing RGB and TIR (thermal infrared) modalities, is widely recognized as a challenging task.<n>It requires not only the effective extraction of features from both modalities and robust fusion strategies, but also the ability to address issues such as spectral discrepancies.<n>We introduce an efficient and easily deployable multispectral object detection framework that can seamlessly optimize high-performing single-modality models.
  arXiv  Detail & Related papers  (2024-11-27T12:18:39Z)
• DetectRL: Benchmarking LLM-Generated Text Detection in Real-World Scenarios [38.952481877244644]
  We present a new benchmark, DetectRL, highlighting that even state-of-the-art (SOTA) detection techniques still underperformed in this task.<n>Using popular large language models (LLMs), we generated data that better aligns with real-world applications.<n>We analyzed the potential impact of writing styles, model types, attack methods, the text lengths, and real-world human writing factors on different types of detectors.
  arXiv  Detail & Related papers  (2024-10-31T09:01:25Z)
• Training-free LLM-generated Text Detection by Mining Token Probability Sequences [18.955509967889782]
  Large language models (LLMs) have demonstrated remarkable capabilities in generating high-quality texts across diverse domains. Training-free methods, which focus on inherent discrepancies through carefully designed statistical features, offer improved generalization and interpretability. We introduce a novel training-free detector, termed textbfLastde that synergizes local and global statistics for enhanced detection.
  arXiv  Detail & Related papers  (2024-10-08T14:23:45Z)
• Efficient Meta-Learning Enabled Lightweight Multiscale Few-Shot Object Detection in Remote Sensing Images [15.12889076965307]
  YOLOv7 one-stage detector is subjected to a novel meta-learning training framework. This transformation allows the detector to adeptly address FSOD tasks while capitalizing on its inherent advantage of lightweight. To validate the effectiveness of our proposed detector, we conducted performance comparisons with current state-of-the-art detectors.
  arXiv  Detail & Related papers  (2024-04-29T04:56:52Z)
• Task-Distributionally Robust Data-Free Meta-Learning [99.56612787882334]
  Data-Free Meta-Learning (DFML) aims to efficiently learn new tasks by leveraging multiple pre-trained models without requiring their original training data. For the first time, we reveal two major challenges hindering their practical deployments: Task-Distribution Shift ( TDS) and Task-Distribution Corruption (TDC)
  arXiv  Detail & Related papers  (2023-11-23T15:46:54Z)
• MGTBench: Benchmarking Machine-Generated Text Detection [54.81446366272403]
  This paper proposes the first benchmark framework for MGT detection against powerful large language models (LLMs) We show that a larger number of words in general leads to better performance and most detection methods can achieve similar performance with much fewer training samples. Our findings indicate that the model-based detection methods still perform well in the text attribution task.
  arXiv  Detail & Related papers  (2023-03-26T21:12:36Z)
• Discovery-and-Selection: Towards Optimal Multiple Instance Learning for Weakly Supervised Object Detection [86.86602297364826]
  We propose a discoveryand-selection approach fused with multiple instance learning (DS-MIL) Our proposed DS-MIL approach can consistently improve the baselines, reporting state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-10-18T07:06:57Z)
• Stance Detection Benchmark: How Robust Is Your Stance Detection? [65.91772010586605]
  Stance Detection (StD) aims to detect an author's stance towards a certain topic or claim. We introduce a StD benchmark that learns from ten StD datasets of various domains in a multi-dataset learning setting. Within this benchmark setup, we are able to present new state-of-the-art results on five of the datasets.
  arXiv  Detail & Related papers  (2020-01-06T13:37:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.