Identifying Machine-Paraphrased Plagiarism

Wahle, Jan Philip; Ruas, Terry; Foltýnek, Tomáš; Meuschke, Norman; Gipp, Béla

doi:10.22541/au.167528156.61938925/v1

Cited by 7 publications

(4 citation statements)

References 47 publications

(89 reference statements)

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“…Transparency -Explainability. Being a multifaceted concept, the term "transparency" is both used to refer to technical explainability 47,65,77,120,124 as well as organizational openness 68,132,136,137 . Regarding the former, papers underscore the need for mechanistic interpretability 124 and for explaining internal mechanisms in generative models 65 .…”

Section: Governance -Regulationmentioning

confidence: 99%

Ethical and methodological challenges in building morally informed AI systems

Hagendorff

Danks

2022

AI Ethics

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Recent progress in large language models has led to applications that can (at least) simulate possession of full moral agency due to their capacity to report context-sensitive moral assessments in open-domain conversations. However, automating moral decision-making faces several methodological as well as ethical challenges. They arise in the fields of bias mitigation, missing ground truth for moral “correctness”, effects of bounded ethicality in machines, changes in moral norms over time, risks of using morally informed AI systems as actual advice, as well as societal implications an increasing importance of algorithmic moral decision-making would have. This paper comments on all these challenges and provides critical considerations for future research on full artificial moral agency. Importantly, some of the adduced challenges can be met by more careful technology design, but others necessarily require engagement with core problems of meta-ethics.

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Section: Governance -Regulationmentioning

confidence: 99%

Ethical and methodological challenges in building morally informed AI systems

Hagendorff

Danks

2022

AI Ethics

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“…After preprocessing, it contains over 29,000,000 paragraphs. • Machine-Paraphrased Plagiarism: contains 200,767 paragraphs (50% of which are paraphrased using the SpinBot API) extracted from Wikipedia (English) articles [36]. This dataset will be used for the evaluation of representations generated by the proposed model.…”

Section: A Datasetsmentioning

confidence: 99%

Recursively Autoregressive Autoencoder for Pyramidal Text Representation

Babić,

Meštrović

2024

IEEE Access

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We introduce Pyramidal Recursive learning (PyRv), a novel method for text representation learning. This approach constructs a pyramidal hierarchy by recursively building representations of phrases, starting from tokens (characters, subwords, or words). At each level, N representations are recursively combined, resulting in N-1 representations on the level above, abstracting the input text from characters or subwords to words, phrases, and potentially sentences. The proposed method employs two learning approaches: autoencoding and autoregression. The autoencoding head decodes encoded representation pairs, while the autoregressive head predicts neighboring representations on both the left and right. This method exhibits four key properties: hierarchical representation, representation compositionality, representation decodability, and self-supervised learning. To implement and validate the proposed method, we train the Pyramidal Recursive Neural Network (PyRvNN) model. Evaluation metrics include autoencoder decodability, plagiarism detection, memorization, and readability. The accuracy of autoencoder decodability serves as an indicator of the validity of the four key properties. Preliminary assessments demonstrate promising results, particularly in machine-paraphrased plagiarism, text readability, and a memorization experiment where the PyRvNN model is compared against FastText and its variant.

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“…the European Research Council), these would need to be included in future funding schemes, providing adequate support to ecologists. Transparent reporting of the responsible use of AI-generated content in scientific work including detailed (and ideally machine-actionable) provenance information must be part of such an open science approach (Wahle et al, 2023). This would also safeguard gap-filling techniques (Table 1) from misuse via fabricated data.…”

Section: Risk Mitigationmentioning

confidence: 99%

How widespread use of generative AI for images and video can affect the environment and the science of ecology

Rillig,

Mansour,

Hempel

et al. 2024

Preprint

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Generative artificial intelligence (AI) models will have broad impacts on society including the scientific enterprise; ecology and environmental science will be no exception. Here we discuss the potential opportunities and risks of advanced generative AI for visual material (images and video) for ecology and the environment. There are clearly opportunities for positive impacts, related to improved communication, for example; we also see possibilities for ecological research to profit from generative AI (e.g., image gap filling, biodiversity surveys, and improved citizen science). However, there are also risks, threatening to undermine the credibility of our science, mostly related to actions of bad actors, for example in terms of spreading fake information or committing fraud. Risks need to be mitigated at the level of government regulatory measures, but we also highlight what can be done right now, including discussing issues with the next generation of ecologists, and transforming towards radically open science workflows.

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Identifying Machine-Paraphrased Plagiarism

Cited by 7 publications

References 47 publications

Ethical and methodological challenges in building morally informed AI systems

Ethical and methodological challenges in building morally informed AI systems

Recursively Autoregressive Autoencoder for Pyramidal Text Representation

How widespread use of generative AI for images and video can affect the environment and the science of ecology

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