Understanding the Effects of Human-written Paraphrases in LLM-generated Text Detection

• URL: http://arxiv.org/abs/2411.03806v1
• Date: Wed, 06 Nov 2024 10:06:21 GMT
• Title: Understanding the Effects of Human-written Paraphrases in LLM-generated Text Detection
• Authors: Hiu Ting Lau, Arkaitz Zubiaga,
• Abstract summary: Human & LLM Paraphrase Collection (HLPC) is a first-of-its-kind dataset that incorporates human-written texts and paraphrases. We perform classification experiments that incorporate human-written paraphrases, watermarked and non-watermarked LLM-generated documents from GPT and OPT, and LLM-generated paraphrases from DIPPER and BART. Results show that the inclusion of human-written paraphrases has a significant impact of LLM-generated detector performance, promoting TPR@1%FPR with a possible trade-off of AUROC and accuracy.
• Score: 7.242609314791262
• License:
• Abstract: Natural Language Generation has been rapidly developing with the advent of large language models (LLMs). While their usage has sparked significant attention from the general public, it is important for readers to be aware when a piece of text is LLM-generated. This has brought about the need for building models that enable automated LLM-generated text detection, with the aim of mitigating potential negative outcomes of such content. Existing LLM-generated detectors show competitive performances in telling apart LLM-generated and human-written text, but this performance is likely to deteriorate when paraphrased texts are considered. In this study, we devise a new data collection strategy to collect Human & LLM Paraphrase Collection (HLPC), a first-of-its-kind dataset that incorporates human-written texts and paraphrases, as well as LLM-generated texts and paraphrases. With the aim of understanding the effects of human-written paraphrases on the performance of state-of-the-art LLM-generated text detectors OpenAI RoBERTa and watermark detectors, we perform classification experiments that incorporate human-written paraphrases, watermarked and non-watermarked LLM-generated documents from GPT and OPT, and LLM-generated paraphrases from DIPPER and BART. The results show that the inclusion of human-written paraphrases has a significant impact of LLM-generated detector performance, promoting TPR@1%FPR with a possible trade-off of AUROC and accuracy.

Related papers

• 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)
• 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-AI 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)
• 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)
• TM-TREK at SemEval-2024 Task 8: Towards LLM-Based Automatic Boundary Detection for Human-Machine Mixed Text [0.0]
  This paper explores the ability of large language models to identify boundaries in human-written and machine-generated mixed texts. Our ensemble model of LLMs achieved first place in the 'Human-Machine Mixed Text Detection' sub-task of the SemEval'24 Competition Task 8.
  arXiv  Detail & Related papers  (2024-04-01T03:54:42Z)
• LLM-Detector: Improving AI-Generated Chinese Text Detection with Open-Source LLM Instruction Tuning [4.328134379418151]
  Existing AI-generated text detection models are prone to in-domain over-fitting. We propose LLM-Detector, a novel method for both document-level and sentence-level text detection.
  arXiv  Detail & Related papers  (2024-02-02T05:54:12Z)
• A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions [39.36381851190369]
  There is an imperative need to develop detectors that can detect LLM-generated text. This is crucial to mitigate potential misuse of LLMs and safeguard realms like artistic expression and social networks from harmful influence of LLM-generated content. The detector techniques have witnessed notable advancements recently, propelled by innovations in watermarking techniques, statistics-based detectors, neural-base detectors, and human-assisted methods.
  arXiv  Detail & Related papers  (2023-10-23T09:01:13Z)
• Source Attribution for Large Language Model-Generated Data [57.85840382230037]
  It is imperative to be able to perform source attribution by identifying the data provider who contributed to the generation of a synthetic text. We show that this problem can be tackled by watermarking. We propose a source attribution framework that satisfies these key properties due to our algorithmic designs.
  arXiv  Detail & Related papers  (2023-10-01T12:02:57Z)
• 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)
• The Science of Detecting LLM-Generated Texts [47.49470179549773]
  The emergence of large language models (LLMs) has resulted in the production of texts that are almost indistinguishable from texts written by humans. This has sparked concerns about the potential misuse of such texts, such as spreading misinformation and causing disruptions in the education system. This survey aims to provide an overview of existing LLM-generated text detection techniques and enhance the control and regulation of language generation models.
  arXiv  Detail & Related papers  (2023-02-04T04:49:17Z)

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.