LLM - D etect AI ve: a Tool for Fine-Grained Machine-Generated Text Detection (2024)

Abstract: The ease of access to large language models (LLMs) has enabled a widespread of machine-generated texts, and now it is often hard to tell whether a piece of text was human-written or machine-generated. This raises concerns about potential misuse, particularly within educational and academic domains. Thus, it is important to develop practical systems that can automate the process. Here, we present one such system, LLM-DetectAIve, designed for fine-grained detection. Unlike most previous work on machine-generated text detection, which focused on binary classification, LLM-DetectAIve supports four categories: (i) human-written, (ii) machine-generated, (iii) machine-written, then machine-humanized, and (iv) human-written, then machine-polished. Category (iii) aims to detect attempts to obfuscate the fact that a text was machine-generated, while category (iv) looks for cases where the LLM was used to polish a human-written text, which is typically acceptable in academic writing, but not in education. Our experiments show that LLM-DetectAIve can effectively identify the above four categories, which makes it a potentially useful tool in education, academia, and other domains.LLM-DetectAIve is publicly accessible at https://github.com/mbzuai-nlp/LLM-DetectAIve. The video describing our system is available at https://youtu.be/E8eT_bE7k8c.

## dataset to collect the dataset for our multi-way finegrained detector, we first gathered datasets that were curated for binary machine-generated text detection from previous work, and then we extended the data into our four labels by introducing new corresponding generations. sections 2.2 and 2.3 discuss the prompts we used for generation and data cleaning, respectively. ## detection models we trained three detectors by fine-tuning roberta @xcite , deberta @xcite , and distilbert @xcite . deberta is built upon bert and roberta by incorporating disentangled attention mechanisms and an enhanced mask decoder, which improves word representation. eventually, in the demo, we used distilbert, which is a compact and fast variant of bert: 60% faster and 40% smaller, while retaining 97% of bert's language understanding capabilities. ## experiments and evaluation the previous studies have shown that the accuracy of detectors drops substantially when testing on out-of-domain examples @xcite . to alleviate this, we propose three strategies: (i) train multiple domain-specific detectors, each specifically responsible for detecting inputs from one domain, (ii) train one universal detector using more training data across various domains, and (iii) leverage domain-adversarial neural network (dann) for domain adaption. ## demo web application our demo web application has two interfaces: (i) an interface for fine-grained mgt detection, and (ii) a playground for users. ## conclusion and future work in an era of advanced large language models, maintaining the integrity of text poses significant challenges. we presented a system that aims to identify the use of machine-generated text, accurately differentiating human-written text from various types of automatically generated text. unlike previous work, we use a fine-grained classification schema (human-written, machine-generated, machine-written machine-humanized, and human-written machine-polished), which offers insights into the origins of the text, thus enabling trustworthiness. in future work, we plan to improve the domain adversarial neural network (dann) to improve the results even further. we further plan to explore the possibility of using a dann on the text's generator instead of the text's domain to generalize detection across different text generators. using a dann on both the domain and the generator could potentially lead to a truly universal detector. we also aim to expand the classification to include a fifth category: machine-written and human-edited text, enhancing detection capabilities and providing a more comprehensive analysis of text origins. to further improve the system, we also plan to address potential biases in the dataset caused by formatting styles linked to specific domains, such as wikihow and peerread, to ensure better robustness across a broader range of human-written content. last but not least, we want to expand the dataset to encompass a diverse set of languages, enabling the development of a robust multilingual detection model.