P et K az at S em E val-2024 Task 8: Can Linguistics Capture the Specifics of LLM -generated Text? (2024)

Abstract: In this paper, we present our submission to the SemEval-2024 Task 8 “Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection”, focusing on the detection of machine-generated texts (MGTs) in English. Specifically, our approach relies on combining embeddings from the RoBERTa-base with diversity features and uses a resampled training set. We score 16th from 139 in the ranking for Subtask A, and our results show that our approach is generalizable across unseen models and domains, achieving an accuracy of 0.91.

## introduction semeval-2024 task 8 "multigenerator, multidomain, and multilingual black-box machine-generated text detection" @xcite has focused on the detection of machine-generated texts (mgts). in recent years, large language models (llms) have achieved human-level performance across multiple tasks, showing impressive capabilities in natural language understanding and generation @xcite , including their abilities to generate high-quality content in such areas as news, social media, question-answering forums, educational, and even academic contexts. often, text generated by llms is almost indistinguishable from that written by humans, especially along such dimensions as text fluency @xcite . therefore, methods of automated mgt detection, intending to mitigate potential misuse of llms, are quickly gaining popularity. automated mgt detection methods can be roughly split into black-box and white-box types, with the former being restricted to api-level access to llms and reliant on features extracted from machine-generated and human-written text samples for classification model training, and the latter focusing on zero-shot for each text, we get a [cls] token embedding from an autoencoder model and extract vectors of linguistic features (e.g., lexical diversity, stylometry, etc.). then, we pass the concatenated vector to a feedforward network, whose output layer performs binary classification -hwt vs. mgt. the configurations of embeddings/features may vary between experiments. ## related work a comprehensive survey by @xcite categorizes detection methods into training-based classifiers, zero-shot detectors, and watermarking techniques, covering both black-box and white-box detection scenarios. this survey discusses a range of strategies, including mixed training, proxy models, and semantic embeddings, indicating ongoing challenges in scalability and robustness. given the fast development of llms and their capabilities, of particular interest are innovations in zero-shot detection methods highlighted by @xcite and @xcite . in addition, @xcite present detectgpt, utilizing perturbation discrepancies to discern mgts, while @xcite propose detectllm-lrr and detectllm-npr, which advance zero-shot detection by harnessing log rank information. another relevant line of research investigates the use of linguistic and stylometric features, such as the ones overviewed in @xcite , for mgt detection. for instance, @xcite explore the use of logistic regression with gltr features (analyzing the distribution of token probabilities and their relative frequencies within specific probability ranges from a language model's output), stylistic characteristics, and nela news verification features (style, complexity, bias, affect, morality, and event specifics) on the m4 dataset, and @xcite introduce a model exploiting text coherence, named entities and relation-aware graph convolutional networks under a low-resource setting for mgt detection. ## methodology our general pipeline, visualized in figure 1 , consists of the following components: (1) an autoencoder model fine-tuned on hwt vs. mgt classification task; (2) linguistic features extraction pipeline; and (3) embeddings and features combination passed through a feed-forward neural network. below we describe some of these components in more detail. ## data shared task organizers have used an extension of the m4 dataset @xcite ), foot_5 which covers a range of domains (including wikihow, wikipedia, reddit, arxiv, peerread, and outfox) and texts generated by a number of llms (including chatgpt, cohere, davinci003, dolly-v2, bloomz, and gpt-4) as well as written by humans. overall, the training set is roughly balanced between hwts and mgts, with 53% being hwts and with the number of hwts being around 5 times higher than that of texts generated by any single llm for each of the domains. the only exception is peerread, where the distribution of texts generated by each llm and written by humans is about the same. at the same time, the distribution is exactly 50%:50% for hwts:mgts in the development set, and 47.5%:52.5% for hwts:mgts in the test set. in addition, while both training and development sets cover a range of domains, the test set is limited to outfox only. a curious case of wikihow before running the experiments, we further investigate how the training data is composed. according to @xcite , llms were provided with relatively short inputs to generate texts across various domains: for example, with titles for wikipedia articles and arxiv papers, titles and abstracts for peerread articles, etc. on the one hand, we observe a high level of parallelism in the training data across hwts and texts generated by various models, and on the other, we note that there is little consistency in what models generate in certain domains: for example, provided with a name of a personality they generate quite different wikipedia entries, which do not only differ from the correspondent hwts but also vary from one llm to another (see examples in appendix b, ## results ## conclusions when developing the models for our submission to the semeval-2024 task 8, we have primarily focused on: (1) the contribution of linguistic features to the task, and (2) the selection of the informative training data. our results suggest that models using only linguistic features (specifically, those based on stylometry and entity grid) can perform competitively on this task, while careful selection of the training data helps improve the performance of the models that rely on embeddings. this shared task demonstrates that it is possible to distinguish between hwts and mgts, but the results also suggest promising avenues for future research, including in-depth analysis of the training data selection techniques and expansion of the linguistic features.