1 code implementation • 29 Aug 2019 • Evan Crothers, Nathalie Japkowicz, Herna Viktor
The detection of clandestine efforts to influence users in online communities is a challenging problem with significant active development.
no code implementations • 1 Jun 2020 • Zois Boukouvalas, Christine Mallinson, Evan Crothers, Nathalie Japkowicz, Aritran Piplai, Sudip Mittal, Anupam Joshi, Tülay Adalı
Social media has become an important communication channel during high impact events, such as the COVID-19 pandemic.
1 code implementation • 2 Mar 2022 • Evan Crothers, Nathalie Japkowicz, Herna Viktor, Paula Branco
The detection of computer-generated text is an area of rapidly increasing significance as nascent generative models allow for efficient creation of compelling human-like text, which may be abused for the purposes of spam, disinformation, phishing, or online influence campaigns.
no code implementations • 13 Oct 2022 • Evan Crothers, Nathalie Japkowicz, Herna Viktor
Detection of machine generated text is a key countermeasure for reducing abuse of NLG models, with significant technical challenges and numerous open problems.
1 code implementation • 13 Jan 2023 • Evan Crothers, Herna Viktor, Nathalie Japkowicz
We apply a large multilingual language model (BLOOM-176B) in open-ended generation of Chinese song lyrics, and evaluate the resulting lyrics for coherence and creativity using human reviewers.
no code implementations • 13 Aug 2023 • Evan Crothers, Herna Viktor, Nathalie Japkowicz
A common approach to quantifying model interpretability is to calculate faithfulness metrics based on iteratively masking input tokens and measuring how much the predicted label changes as a result.