Detecting Machine-Generated Text Across Diverse Domains and Language Models

Conceptos Básicos

Combining linguistic features and language model embeddings can effectively distinguish machine-generated text from human-written text, even across unseen language models and domains.

Resumen

The paper presents a system for detecting machine-generated text (MGT) in the SemEval-2024 Task 8 on "Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection". The authors investigate the impact of various linguistic features, including text statistics, readability, stylometry, lexical diversity, rhetorical structure, and entity grid, on the detection task. They find that a combination of embeddings from a fine-tuned RoBERTa-base model and lexical diversity features achieves the best performance, outperforming a competitive baseline. The authors also observe that a model relying solely on linguistic features, such as stylometry and entity grid, can perform on par with the baseline. Additionally, the authors discuss the importance of careful selection of the training data, noting that using MGTs from all domains and human-written texts (HWTs) only from the WikiHow domain leads to improved performance. The results demonstrate the generalizability of the proposed approach, as it achieves high accuracy on unseen language models and domains.

Estadísticas

The number of difficult words (words with more than two syllables and not in the list of easy words) is lower in HWTs compared to MGTs across all language models.
The raw lexicon count (unique words) and raw sentence count are higher in HWTs compared to MGTs across all language models.
The Flesch Reading Ease Test, Flesch-Kincaid Grade Level Test, and Linsear Write Metric indicate that HWTs are generally more readable than MGTs.

Citas

"Our results suggest that our best model, which uses diversity features and embeddings, outperforms a very competitive baseline introduced in this task (Wang et al., 2024), yielding an accuracy of 0.95 on the development and 0.91 on the test set."
"It is the only feature type that increases the accuracy obtained with embeddings only."
"Stylometry features turn out to be the best linguistic feature type when used on their own: the accuracy with sty is 0.68 vs. 0.6 with feat."

Ideas clave extraídas de

PetKaz at SemEval-2024 Task 8

by Kseniia Petu... a las arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.05483.pdf

Consultas más profundas

How can the proposed approach be extended to detect machine-generated text in other languages beyond English?

The proposed approach can be extended to detect machine-generated text in other languages by adapting the linguistic features and training data selection techniques to suit the characteristics of those languages. Firstly, the model can be fine-tuned on LLMs trained on different languages to capture language-specific patterns in machine-generated text. Linguistic features such as stylometry, entity grid analysis, and lexical diversity can be adjusted to account for language-specific nuances. Additionally, the training data should include a diverse range of texts in the target languages to ensure the model's generalizability.

What other linguistic features or techniques could be explored to further improve the detection of machine-generated text, especially in cases where the models struggle, such as with texts from the PeerRead domain?

To enhance the detection of machine-generated text, especially in challenging domains like PeerRead, additional linguistic features and techniques can be explored. One approach could involve incorporating discourse analysis features to capture the structural organization of texts. Features related to coherence, cohesion, and rhetorical relations can provide valuable insights into the differences between human-written and machine-generated texts. Furthermore, sentiment analysis features could be utilized to detect subtle emotional cues that may differ between human and machine-generated content in complex domains like PeerRead.

Given the observed differences in the consistency of machine-generated text across domains, how can this information be leveraged to develop more robust and generalizable detection methods?

The observed differences in the consistency of machine-generated text across domains can be leveraged to develop more robust and generalizable detection methods by implementing domain-specific fine-tuning strategies. By training the detection model on a diverse set of texts from various domains, the model can learn to adapt to the specific characteristics of each domain. Additionally, ensemble learning techniques can be employed to combine the strengths of multiple models trained on different domains, improving the overall detection performance. Leveraging domain-specific data augmentation techniques and transfer learning approaches can also enhance the model's ability to generalize across diverse domains.