MasonTigers at SemEval-2024 Task 8: Performance Analysis of Transformer-based Models on Machine-Generated Text Detection
Not on the map. Why?
Abstract (arXiv)
This paper presents the MasonTigers entry to the SemEval-2024 Task 8 - Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection. The task encompasses Binary Human-Written vs. Machine-Generated Text Classification (Track A), Multi-Way Machine-Generated Text Classification (Track B), and Human-Machine Mixed Text Detection (Track C). Our best performing approaches utilize mainly the ensemble of discriminator transformer models along with sentence transformer and statistical machine learning approaches in specific cases. Moreover, zero-shot prompting and fine-tuning of FLAN-T5 are used for Track A and B.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 27 of its 45 references that are in the map. First posted on arXiv 22 Mar 2024.
Builds on
27 of 45 references.
References · 27
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
- 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
- 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,355
- 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,157
- 2021DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharingcited by 1,219
- 2023DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvaturecited by 705
- 2023How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detectioncited by 472
- 2023Can AI-Generated Text be Reliably Detected?cited by 372
- 2023Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvaturecited by 278
- 2019Automatic Detection of Generated Text is Easiest when Humans are Fooledcited by 260
15 more
- 2018WikiHow: A Large Scale Text Summarization Datasetcited by 203
- 2018A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applicationscited by 135
- 2023M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detectioncited by 118
- 2021Larger-Scale Transformers for Multilingual Masked Language Modelingcited by 86
- 2022Real or Fake Text?: Investigating Human Ability to Detect Boundaries Between Human-Written and Machine-Generated Textcited by 63
- 2023Contrasting Linguistic Patterns in Human and LLM-Generated News Textcited by 62
- 2024M4GT-Bench: Evaluation Benchmark for Black-Box Machine-Generated Text Detectioncited by 43
- 2020It's not Greek to mBERT: Inducing Word-Level Translations from Multilingual BERTcited by 30
- 2022Findings of the The RuATD Shared Task 2022 on Artificial Text Detection in Russiancited by 29
- 2021Unsupervised and Distributional Detection of Machine-Generated Textcited by 23
- 2024LLM-Detector: Improving AI-Generated Chinese Text Detection with Open-Source LLM Instruction Tuningcited by 10
- 2020Transformer based Multilingual document Embedding modelcited by 4
- 2023STADEE: STAtistics-based DEEp Detection of Machine Generated Textcited by 4
- 2024MasonPerplexity at ClimateActivism 2024: Integrating Advanced Ensemble Techniques and Data Augmentation for Climate Activism Stance and Hate Event Identificationcited by 3
- 2023nlpBDpatriots at BLP-2023 Task 2: A Transfer Learning Approach to Bangla Sentiment Analysiscited by 2
Built on by
Citing papers per year: 2024: 1.
1 paper.