Author: (1) Ram Mohan Rao Kadiyala, University of Maryland, College Park (rkadiyal@terpmail.umd.edu). Author: Author: (1) Ram Mohan Rao Kadiyala, University of Maryland, College Park (rkadiyal@terpmail.umd.edu). Table of Links Abstract and Introduction Dataset 2.1 Baseline 2.2 Proposed Model 2.3 Our System 2.4 Results 2.5 Comparison With Proprietary Systems 2.6 Results Comparison Conclusion 3.1 Strengths and Weaknesses 3.2 Possible Improvements 3.3 Possible Extensions and Applications 3.4 Limitations and Potential for Misuse Abstract and Introduction Abstract and Introduction Abstract and Introduction Dataset 2.1 Baseline 2.2 Proposed Model 2.3 Our System 2.4 Results 2.5 Comparison With Proprietary Systems 2.6 Results Comparison Dataset Dataset 2.1 Baseline Baseline 2.2 Proposed Model Proposed Model 2.3 Our System Our System 2.4 Results Results 2.5 Comparison With Proprietary Systems Comparison With Proprietary Systems 2.6 Results Comparison Results Comparison Conclusion 3.1 Strengths and Weaknesses 3.2 Possible Improvements 3.3 Possible Extensions and Applications 3.4 Limitations and Potential for Misuse Conclusion Conclusion 3.1 Strengths and Weaknesses Strengths and Weaknesses 3.2 Possible Improvements Possible Improvements 3.3 Possible Extensions and Applications Possible Extensions and Applications 3.4 Limitations and Potential for Misuse Limitations and Potential for Misuse A. Other Plots and information A. Other Plots and information B. System Description B. System Description C. Effect of Text boundary location on performance C. Effect of Text boundary location on performance Abstract With increasing usage of generative models for text generation and widespread use of machine generated texts in various domains, being able to distinguish between human written and machine generated texts is a significant challenge. While existing models and proprietary systems focus on identifying whether given text is entirely human written or entirely machine generated, only a few systems provide insights at sentence or paragraph level at likelihood of being machine generated at a non reliable accuracy level, working well only for a set of domains and generators. This paper introduces few reliable approaches for the novel task of identifying which part of a given text is machine generated at a word level while comparing results from different approaches and methods. We present a comparison with proprietary systems , performance of our model on unseen domains’ and generators’ texts. The findings reveal significant improvements in detection accuracy along with comparison on other aspects of detection capabilities. Finally we discuss potential avenues for improvement and implications of our work. The proposed model is also well suited for detecting which parts of a text are machine generated in outputs of Instruct variants of many LLMs. 1 Introduction With rapid advancements and usage of AI models for text generation , being able to distinguish machine generated texts from human generated texts is gaining importance. While existing models and proprietary systems like GLTR (Gehrmann et al., 2019), ZeroGPT (ZeroGPT), GPTZero (Tian and Cui, 2023), GPTKit (GptKit), Open AI detector , etc.. focus on detecting whether a given text is entirely AI written or entirely human written , there was less advancement in detecting which parts of a given text are AI written in a partially machine generated text. While some of the above mentioned systems provide insights into which parts of the given text are likely AI generated , these are often found to be unreliable and having an accuracy close or worse than random guessing. There is also a rise in usage of AI to spread fake news and misinformation along with using AI models to modify Wikipedia articles (Vice, 2023). Our proposed model focuses on detecting word level text boundary in partially machine generated texts as part of the SemEval shared task : Multi-generator, Multi-domain, and Multilingual Black-Box Machine-Generated Text Detection(Wang et al., 2024b). This paper also discusses implications of findings , comparisons with different models and approaches, comparison with existing proprietary systems with relevant metrics , other findings regarding AI generated texts. The official submission is DeBERTa-CRF several other models have been tested for comparison. With new, better, and diverse AI models coming into existence, having a model that can make accurate predictions on unseen domains and unseen generator texts can be useful for practical scenarios. This paper is available on arxiv under CC BY-NC-SA 4.0 license. This paper is available on arxiv under CC BY-NC-SA 4.0 license. available on arxiv
