SemEval-2024 Task 8: Weighted Layer Averaging RoBERTa for Black-Box Machine-Generated Text Detection
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Abstract (arXiv)
This document contains the details of the authors' submission to the proceedings of SemEval 2024's Task 8: Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection Subtask A (monolingual) and B. Detection of machine-generated text is becoming an increasingly important task, with the advent of large language models (LLMs). In this paper, we lay out how using weighted averages of RoBERTa layers lets us capture information about text that is relevant to machine-generated text detection.
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 5 of its 9 references that are in the map. First posted on arXiv 24 Feb 2024.
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5 of 9 references.
References · 5
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,231
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,322
- 2020A Primer in BERTology: What we know about how BERT workscited by 809
- 2020Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuningcited by 665
- 2023M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detectioncited by 120
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Citing papers per year: 2024: 1.
1 paper.