PetKaz at SemEval-2024 Task 8: Can Linguistics Capture the Specifics of LLM-generated Text?

Kseniia Petukhova, Roman Kazakov, Ekaterina Kochmar
4/8/2024
cs.CLcs.AI

No confident code implementation found for this paper.

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 12th from 124 in the ranking for Subtask A (monolingual track), and our results show that our approach is generalizable across unseen models and domains, achieving an accuracy of 0.91.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection

89% match1 repo822024

Fine-tuning Large Language Models for Multigenerator, Multidomain, and Multilingual Machine-Generated Text Detection

88% match2024

KInIT at SemEval-2024 Task 8: Fine-tuned LLMs for Multilingual Machine-Generated Text Detection

88% match2 repos222024

TrustAI at SemEval-2024 Task 8: A Comprehensive Analysis of Multi-domain Machine Generated Text Detection Techniques

88% match2024

Mast Kalandar at SemEval-2024 Task 8: On the Trail of Textual Origins: RoBERTa-BiLSTM Approach to Detect AI-Generated Text

88% match2024

SemEval-2024 Task 8: Weighted Layer Averaging RoBERTa for Black-Box Machine-Generated Text Detection

87% match1 repo2024

Cite this paper

@article{petukhova2024petkaz,
  title  = {PetKaz at SemEval-2024 Task 8: Can Linguistics Capture the Specifics of LLM-generated Text?},
  author = {Kseniia Petukhova and Roman Kazakov and Ekaterina Kochmar},
  year   = {2024},
  eprint = {2404.05483},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.05483v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2404.05483.svg)](https://codeofpaper.com/paper/2404.05483)

Discussion