AI Usage Cards: Responsibly Reporting AI-generated Content
February 16, 2023
Given AI systems like ChatGPT can generate content that is indistinguishable from human-made work, the responsible use of this technology is a growing concern. Although
my NLP & AI Research
Welcome to our collection of articles, research papers, and thought-provoking pieces on natural language processing and artificial intelligence. Our publications showcase the latest breakthroughs in AI research, and share insights into the social and ethical implications of these technologies. Join us in exploring the fascinating world of AI research and stay up-to-date with our collection of AI publications. For statistics about the papers, check out Google Scholar or Semantic Scholar. Consider visiting the blog for a more accessible reading experience.
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Analyzing Multi-Task Learning for Abstractive Text Summarization
December 7, 2022
Despite the recent success of multi-task learning and pre-finetuning for natural language understanding, few works have studied the effects of task families on abstractive text
How Large Language Models are Transforming Machine-Paraphrase Plagiarism
December 7, 2022
The recent success of large language models for text generation poses a severe threat to academic integrity, as plagiarists can generate realistic paraphrases indistinguishable from
D3: A Massive Dataset of Scholarly Metadata for Analyzing the State of Computer Science Research
June 20, 2022
DBLP is the largest open-access repository of scientific articles on computer science and provides metadata associated with publications, authors, and venues. We retrieved more than
Testing the Generalization of Neural Language Models for COVID-19 Misinformation Detection
February 23, 2022
A drastic rise in potentially life-threatening misinformation has been a by-product of the COVID-19 pandemic. Computational support to identify false information within the massive body
Identifying Machine-Paraphrased Plagiarism
February 23, 2022
Employing paraphrasing tools to conceal plagiarized text is a severe threat to academic integrity. To enable the detection of machine-paraphrased text, we evaluate the effectiveness