EVIL-Detect for NLPCC 2026 Shared Task 6: LLM-Generated Text Detection

• URL: http://arxiv.org/abs/2608.10698v1
• Date: Tue, 11 Aug 2026 09:19:56 GMT
• Title: EVIL-Detect for NLPCC 2026 Shared Task 6: LLM-Generated Text Detection
• Abstract summary: This paper presents EVIL-Detect, a multi-signal ensemble framework with conflict-aware fusion for NLPCC 2026 Shared Task 6.<n>The system integrates edit-extent regression, zero-shot likelihood-contrast signals, lexical statistics, and conservative text rules.<n>Our system improves robustness under strong out-of-distribution shifts, achieving a macro-F1 score of 0.8888 and ranking first in the official evaluation.
• Score: 11.65568345337734
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid development of large language models (LLMs) has increased the need for reliable detection of LLM-generated text, especially in realistic Chinese scenarios involving human-written text (HWT), LLM-generated text (LGT), and LLM-refined text (HLT). This paper presents EVIL-Detect, a multi-signal ensemble framework with conflict-aware fusion for NLPCC 2026 Shared Task 6. The system integrates edit-extent regression, zero-shot likelihood-contrast signals, lexical statistics, and conservative text rules. With calibrated decision boundaries and conflict-aware integration, our system improves robustness under strong out-of-distribution shifts, achieving a macro-F1 score of 0.8888 and ranking first in the official evaluation. Our code is available at https://github.com/bbbbhrrrr/evildetect.

Related papers

• EnsemJudge: Enhancing Reliability in Chinese LLM-Generated Text Detection through Diverse Model Ensembles [16.37055946884028]
  EnsemJudge is a robust framework for detecting Chinese LLM-generated text.<n>We trained and evaluated our system on a carefully constructed Chinese dataset provided by NLPCC2025 Shared Task 1.
  arXiv  Detail & Related papers  (2026-03-30T01:50:46Z)
• HLPD: Aligning LLMs to Human Language Preference for Machine-Revised Text Detection [3.090546888821788]
  We propose Human Language Preference Detection (HLPD) to detect texts generated by machine-revised text.<n>HLPD employs a reward-based alignment process, Human Language Preference Optimization (HLPO), to shift the scoring model's token distribution toward human-like writing.<n>When detecting texts revised by GPT-series models, HLPD achieves a 15.11% relative improvement in AUROC over ImBD, surpassing Fast-DetectGPT by 45.56%.
  arXiv  Detail & Related papers  (2025-11-10T10:47:34Z)
• TextualVerifier: Verify TextGrad Step-by-Step [0.25489046505746704]
  TextualVerifier implements chain-of-thought decomposition, variant generation, majority voting, and consensus aggregation.<n>It integrates non-invasively with TextGrad at both the loss function and optimization result verification stages.
  arXiv  Detail & Related papers  (2025-10-29T13:53:42Z)
• RepreGuard: Detecting LLM-Generated Text by Revealing Hidden Representation Patterns [50.401907401444404]
  Large language models (LLMs) are crucial for preventing misuse and building trustworthy AI systems.<n>We propose RepreGuard, an efficient statistics-based detection method.<n> Experimental results show that RepreGuard outperforms all baselines with average 94.92% AUROC on both in-distribution (ID) and OOD scenarios.
  arXiv  Detail & Related papers  (2025-08-18T17:59:15Z)
• Robust Detection of LLM-Generated Text: A Comparative Analysis [0.276240219662896]
  Large language models can be widely integrated into many aspects of life, and their output can quickly fill all network resources. It becomes increasingly important to develop powerful detectors for the generated text. This detector is essential to prevent the potential misuse of these technologies and to protect areas such as social media from the negative effects.
  arXiv  Detail & Related papers  (2024-11-09T18:27:15Z)
• ReMoDetect: Reward Models Recognize Aligned LLM's Generations [55.06804460642062]
  Large language models (LLMs) generate human-preferable texts. In this paper, we identify the common characteristics shared by these models. We propose two training schemes to further improve the detection ability of the reward model.
  arXiv  Detail & Related papers  (2024-05-27T17:38:33Z)
• Who Wrote This? The Key to Zero-Shot LLM-Generated Text Detection Is GECScore [51.65730053591696]
  We propose a simple yet effective black-box zero-shot detection approach based on the observation that human-written texts typically contain more grammatical errors than LLM-generated texts.<n> Experimental results show that our method outperforms current state-of-the-art (SOTA) zero-shot and supervised methods.
  arXiv  Detail & Related papers  (2024-05-07T12:57:01Z)
• Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text [98.28130949052313]
  A score based on contrasting two closely related language models is highly accurate at separating human-generated and machine-generated text. We propose a novel LLM detector that only requires simple calculations using a pair of pre-trained LLMs. The method, called Binoculars, achieves state-of-the-art accuracy without any training data.
  arXiv  Detail & Related papers  (2024-01-22T16:09:47Z)
• Red Teaming Language Model Detectors with Language Models [114.36392560711022]
  Large language models (LLMs) present significant safety and ethical risks if exploited by malicious users. Recent works have proposed algorithms to detect LLM-generated text and protect LLMs. We study two types of attack strategies: 1) replacing certain words in an LLM's output with their synonyms given the context; 2) automatically searching for an instructional prompt to alter the writing style of the generation.
  arXiv  Detail & Related papers  (2023-05-31T10:08:37Z)
• LLMDet: A Third Party Large Language Models Generated Text Detection Tool [119.0952092533317]
  Large language models (LLMs) are remarkably close to high-quality human-authored text. Existing detection tools can only differentiate between machine-generated and human-authored text. We propose LLMDet, a model-specific, secure, efficient, and extendable detection tool.
  arXiv  Detail & Related papers  (2023-05-24T10:45:16Z)

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.