Towards Reliable Detection of LLM-Generated Texts: A Comprehensive Evaluation Framework with CUDRT

• URL: http://arxiv.org/abs/2406.09056v3
• Date: Tue, 17 Dec 2024 12:20:34 GMT
• Title: Towards Reliable Detection of LLM-Generated Texts: A Comprehensive Evaluation Framework with CUDRT
• Authors: Zhen Tao, Yanfang Chen, Dinghao Xi, Zhiyu Li, Wei Xu,
• Abstract summary: Large language models (LLMs) have significantly advanced text generation, but the human-like quality of their outputs presents major challenges. We propose CUDRT, a comprehensive evaluation framework and bilingual benchmark in Chinese and English. This framework supports scalable, reproducible experiments and enables analysis of how operational diversity, multilingual training sets, and LLM architectures influence detection performance.
• Score: 9.682499180341273
• License:
• Abstract: The increasing prevalence of large language models (LLMs) has significantly advanced text generation, but the human-like quality of LLM outputs presents major challenges in reliably distinguishing between human-authored and LLM-generated texts. Existing detection benchmarks are constrained by their reliance on static datasets, scenario-specific tasks (e.g., question answering and text refinement), and a primary focus on English, overlooking the diverse linguistic and operational subtleties of LLMs. To address these gaps, we propose CUDRT, a comprehensive evaluation framework and bilingual benchmark in Chinese and English, categorizing LLM activities into five key operations: Create, Update, Delete, Rewrite, and Translate. CUDRT provides extensive datasets tailored to each operation, featuring outputs from state-of-the-art LLMs to assess the reliability of LLM-generated text detectors. This framework supports scalable, reproducible experiments and enables in-depth analysis of how operational diversity, multilingual training sets, and LLM architectures influence detection performance. Our extensive experiments demonstrate the framework's capacity to optimize detection systems, providing critical insights to enhance reliability, cross-linguistic adaptability, and detection accuracy. By advancing robust methodologies for identifying LLM-generated texts, this work contributes to the development of intelligent systems capable of meeting real-world multilingual detection challenges. Source code and dataset are available at GitHub.

Related papers

• "I know myself better, but not really greatly": Using LLMs to Detect and Explain LLM-Generated Texts [10.454446545249096]
  Large language models (LLMs) have demonstrated impressive capabilities in generating human-like texts. This paper explores the detection and explanation capabilities of LLM-based detectors of human-generated texts.
  arXiv  Detail & Related papers  (2025-02-18T11:00:28Z)
• VilBias: A Study of Bias Detection through Linguistic and Visual Cues , presenting Annotation Strategies, Evaluation, and Key Challenges [2.2751168722976587]
  VLBias is a framework that leverages state-of-the-art Large Language Models (LLMs) and Vision-Language Models (VLMs) to detect linguistic and visual biases in news content. We present a multimodal dataset comprising textual content and corresponding images from diverse news sources.
  arXiv  Detail & Related papers  (2024-12-22T15:05:30Z)
• 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)
• GigaCheck: Detecting LLM-generated Content [72.27323884094953]
  In this work, we investigate the task of generated text detection by proposing the GigaCheck. Our research explores two approaches: (i) distinguishing human-written texts from LLM-generated ones, and (ii) detecting LLM-generated intervals in Human-Machine collaborative texts. Specifically, we use a fine-tuned general-purpose LLM in conjunction with a DETR-like detection model, adapted from computer vision, to localize AI-generated intervals within text.
  arXiv  Detail & Related papers  (2024-10-31T08:30:55Z)
• Think Carefully and Check Again! Meta-Generation Unlocking LLMs for Low-Resource Cross-Lingual Summarization [108.6908427615402]
  Cross-lingual summarization ( CLS) aims to generate a summary for the source text in a different target language. Currently, instruction-tuned large language models (LLMs) excel at various English tasks. Recent studies have shown that LLMs' performance on CLS tasks remains unsatisfactory even with few-shot settings.
  arXiv  Detail & Related papers  (2024-10-26T00:39:44Z)
• Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurement [51.601916604301685]
  Large language models (LLMs) generate content that can undermine trust in online discourse. Current methods often focus on binary classification, failing to address the complexities of real-world scenarios like human-LLM collaboration. To move beyond binary classification and address these challenges, we propose a new paradigm for detecting LLM-generated content.
  arXiv  Detail & Related papers  (2024-10-18T08:14:10Z)
• Unveiling Large Language Models Generated Texts: A Multi-Level Fine-Grained Detection Framework [9.976099891796784]
  Large language models (LLMs) have transformed human writing by enhancing grammar correction, content expansion, and stylistic refinement. Existing detection methods, which mainly rely on single-feature analysis and binary classification, often fail to effectively identify LLM-generated text in academic contexts. We propose a novel Multi-level Fine-grained Detection framework that detects LLM-generated text by integrating low-level structural, high-level semantic, and deep-level linguistic features.
  arXiv  Detail & Related papers  (2024-10-18T07:25:00Z)
• PPTC-R benchmark: Towards Evaluating the Robustness of Large Language Models for PowerPoint Task Completion [96.47420221442397]
  We construct adversarial user instructions by attacking user instructions at sentence, semantic, and multi-language levels. We test 3 closed-source and 4 open-source LLMs using a benchmark that incorporates robustness settings. We find that GPT-4 exhibits the highest performance and strong robustness in our benchmark.
  arXiv  Detail & Related papers  (2024-03-06T15:33:32Z)
• Lost in the Source Language: How Large Language Models Evaluate the Quality of Machine Translation [64.5862977630713]
  This study investigates how Large Language Models (LLMs) leverage source and reference data in machine translation evaluation task. We find that reference information significantly enhances the evaluation accuracy, while surprisingly, source information sometimes is counterproductive.
  arXiv  Detail & Related papers  (2024-01-12T13:23:21Z)
• Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
  Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
  arXiv  Detail & Related papers  (2023-12-26T07:24:46Z)

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.